No history yet

Introduction to Election Polls

What Are Election Polls?

Election polls are surveys designed to take the political temperature of the public. They aim to capture a snapshot of what a group of people thinks about candidates or issues at a specific moment in time. Think of it like a doctor checking a patient's vital signs. The results aren't a guarantee of future health, but they provide valuable information about the current condition.

The main purpose of a poll is to understand public opinion without having to ask every single person. For candidates, polls help shape campaign strategy. For the public and the media, they offer a glimpse into the potential outcome of an election.

Lesson image

Gathering Opinions

Since it’s impossible to survey an entire country, pollsters use a technique called sampling. Instead of talking to millions of voters, they select a smaller group that is intended to be a miniature version of the entire voting population.

Imagine trying to figure out if a giant pot of soup is salty enough. You don't need to drink the whole thing. You just taste a single spoonful. A good sample is like that spoonful—it should have a little bit of everything in the pot.

The goal is to create a representative sample, where the demographics of the small group (like age, gender, location, and race) match the proportions of the larger population. The most common method is random sampling, where every person in the population has an equal chance of being chosen. This helps avoid bias and makes it more likely the results reflect the views of the population as a whole.

\n\x3C!-- This file was generated by dvisvgm 2.11.1 -->\n\x3Csvg version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' width='226.771654pt' height='139.801427pt' viewBox='102.802214 111.310835 226.771654 139.801427'>\n\x3Cdefs>\n\x3Cpath id='g1-40' d='M3.297634 2.391034C3.297634 2.361146 3.297634 2.34122 3.128269 2.171856C1.882939 .916563 1.564134-.966376 1.564134-2.49066C1.564134-4.224159 1.942715-5.957659 3.16812-7.202989C3.297634-7.32254 3.297634-7.342466 3.297634-7.372354C3.297634-7.442092 3.257783-7.47198 3.198007-7.47198C3.098381-7.47198 2.201743-6.794521 1.613948-5.529265C1.105853-4.433375 .986301-3.327522 .986301-2.49066C.986301-1.713574 1.09589-.508095 1.643836 .617684C2.241594 1.843088 3.098381 2.49066 3.198007 2.49066C3.257783 2.49066 3.297634 2.460772 3.297634 2.391034Z'/>\n\x3Cpath id='g1-41' d='M2.879203-2.49066C2.879203-3.267746 2.769614-4.473225 2.221669-5.599004C1.62391-6.824408 .767123-7.47198 .667497-7.47198C.607721-7.47198 .56787-7.43213 .56787-7.372354C.56787-7.342466 .56787-7.32254 .757161-7.143213C1.733499-6.156912 2.30137-4.572852 2.30137-2.49066C2.30137-.787049 1.932752 .966376 .697385 2.221669C.56787 2.34122 .56787 2.361146 .56787 2.391034C.56787 2.450809 .607721 2.49066 .667497 2.49066C.767123 2.49066 1.663761 1.8132 2.251557 .547945C2.759651-.547945 2.879203-1.653798 2.879203-2.49066Z'/>\n\x3Cpath id='g1-65' d='M3.965131-6.933998C3.915318-7.063512 3.895392-7.13325 3.73599-7.13325S3.5467-7.073474 3.496887-6.933998L1.43462-.976339C1.255293-.468244 .856787-.318804 .318804-.308842V0C.547945-.009963 .976339-.029888 1.334994-.029888C1.643836-.029888 2.161893-.009963 2.480697 0V-.308842C1.982565-.308842 1.733499-.557908 1.733499-.816936C1.733499-.846824 1.743462-.946451 1.753425-.966376L2.211706-2.271482H4.672478L5.200498-.747198C5.210461-.707347 5.230386-.647572 5.230386-.607721C5.230386-.308842 4.672478-.308842 4.403487-.308842V0C4.762142-.029888 5.459527-.029888 5.838107-.029888C6.266501-.029888 6.724782-.019925 7.143213 0V-.308842H6.963885C6.366127-.308842 6.22665-.37858 6.117061-.707347L3.965131-6.933998ZM3.437111-5.818182L4.562889-2.580324H2.321295L3.437111-5.818182Z'/>\n\x3Cpath id='g1-66' d='M2.211706-3.646326V-6.097136C2.211706-6.425903 2.231631-6.495641 2.699875-6.495641H3.935243C4.901619-6.495641 5.250311-5.648817 5.250311-5.120797C5.250311-4.483188 4.762142-3.646326 3.656289-3.646326H2.211706ZM4.562889-3.556663C5.529265-3.745953 6.216687-4.383562 6.216687-5.120797C6.216687-5.987547 5.300125-6.804483 4.004981-6.804483H.358655V-6.495641H.597758C1.364882-6.495641 1.384807-6.386052 1.384807-6.027397V-.777086C1.384807-.418431 1.364882-.308842 .597758-.308842H.358655V0H4.26401C5.589041 0 6.485679-.886675 6.485679-1.823163C6.485679-2.689913 5.668742-3.437111 4.562889-3.556663ZM3.945205-.308842H2.699875C2.231631-.308842 2.211706-.37858 2.211706-.707347V-3.427148H4.084682C5.070984-3.427148 5.489415-2.500623 5.489415-1.833126C5.489415-1.125778 4.971357-.308842 3.945205-.308842Z'/>\n\x3Cpath id='g1-67' d='M.557908-3.407223C.557908-1.344956 2.171856 .219178 4.024907 .219178C5.648817 .219178 6.625156-1.165629 6.625156-2.321295C6.625156-2.420922 6.625156-2.49066 6.495641-2.49066C6.386052-2.49066 6.386052-2.430884 6.37609-2.331258C6.296389-.9066 5.230386-.089664 4.144458-.089664C3.536737-.089664 1.58406-.428394 1.58406-3.39726C1.58406-6.37609 3.526775-6.714819 4.134496-6.714819C5.220423-6.714819 6.107098-5.808219 6.306351-4.353674C6.326276-4.214197 6.326276-4.184309 6.465753-4.184309C6.625156-4.184309 6.625156-4.214197 6.625156-4.423412V-6.784558C6.625156-6.953923 6.625156-7.023661 6.515567-7.023661C6.475716-7.023661 6.435866-7.023661 6.356164-6.90411L5.858032-6.166874C5.489415-6.525529 4.98132-7.023661 4.024907-7.023661C2.161893-7.023661 .557908-5.439601 .557908-3.407223Z'/>\n\x3Cpath id='g1-71' d='M5.907846-.627646C6.03736-.408468 6.435866-.009963 6.545455-.009963C6.635118-.009963 6.635118-.089664 6.635118-.239103V-1.972603C6.635118-2.361146 6.674969-2.410959 7.32254-2.410959V-2.719801C6.953923-2.709838 6.405978-2.689913 6.107098-2.689913C5.708593-2.689913 4.861768-2.689913 4.503113-2.719801V-2.410959H4.821918C5.718555-2.410959 5.748443-2.30137 5.748443-1.932752V-1.295143C5.748443-.179328 4.483188-.089664 4.204234-.089664C3.556663-.089664 1.58406-.438356 1.58406-3.407223C1.58406-6.386052 3.5467-6.714819 4.144458-6.714819C5.210461-6.714819 6.117061-5.818182 6.316314-4.353674C6.336239-4.214197 6.336239-4.184309 6.475716-4.184309C6.635118-4.184309 6.635118-4.214197 6.635118-4.423412V-6.784558C6.635118-6.953923 6.635118-7.023661 6.525529-7.023661C6.485679-7.023661 6.445828-7.023661 6.366127-6.90411L5.867995-6.166874C5.549191-6.485679 5.011208-7.023661 4.024907-7.023661C2.171856-7.023661 .557908-5.449564 .557908-3.407223S2.15193 .219178 4.044832 .219178C4.772105 .219178 5.569116-.039851 5.907846-.627646Z'/>\n\x3Cpath id='g1-77' d='M2.400996-6.585305C2.311333-6.804483 2.281445-6.804483 2.052304-6.804483H.368618V-6.495641H.607721C1.374844-6.495641 1.39477-6.386052 1.39477-6.027397V-1.046077C1.39477-.777086 1.39477-.308842 .368618-.308842V0C.71731-.009963 1.205479-.029888 1.534247-.029888S2.351183-.009963 2.699875 0V-.308842C1.673724-.308842 1.673724-.777086 1.673724-1.046077V-6.41594H1.683686L4.084682-.219178C4.134496-.089664 4.184309 0 4.283935 0C4.393524 0 4.423412-.079701 4.463263-.18929L6.914072-6.495641H6.924035V-.777086C6.924035-.418431 6.90411-.308842 6.136986-.308842H5.897883V0C6.266501-.029888 6.94396-.029888 7.332503-.029888S8.388543-.029888 8.757161 0V-.308842H8.518057C7.750934-.308842 7.731009-.418431 7.731009-.777086V-6.027397C7.731009-6.386052 7.750934-6.495641 8.518057-6.495641H8.757161V-6.804483H7.073474C6.814446-6.804483 6.814446-6.794521 6.744707-6.615193L4.562889-1.006227L2.400996-6.585305Z'/>\n\x3Cpath id='g1-84' d='M6.635118-6.744707H.547945L.358655-4.503113H.607721C.747198-6.107098 .896638-6.435866 2.400996-6.435866C2.580324-6.435866 2.839352-6.435866 2.938979-6.41594C3.148194-6.37609 3.148194-6.266501 3.148194-6.03736V-.787049C3.148194-.448319 3.148194-.308842 2.102117-.308842H1.703611V0C2.11208-.029888 3.128269-.029888 3.58655-.029888S5.070984-.029888 5.479452 0V-.308842H5.080946C4.034869-.308842 4.034869-.448319 4.034869-.787049V-6.03736C4.034869-6.236613 4.034869-6.37609 4.214197-6.41594C4.323786-6.435866 4.592777-6.435866 4.782067-6.435866C6.286426-6.435866 6.435866-6.107098 6.575342-4.503113H6.824408L6.635118-6.744707Z'/>\n\x3Cpath id='g1-97' d='M3.317559-.757161C3.35741-.358655 3.626401 .059776 4.094645 .059776C4.303861 .059776 4.911582-.079701 4.911582-.886675V-1.444583H4.662516V-.886675C4.662516-.308842 4.41345-.249066 4.303861-.249066C3.975093-.249066 3.935243-.697385 3.935243-.747198V-2.739726C3.935243-3.158157 3.935243-3.5467 3.576588-3.915318C3.188045-4.303861 2.689913-4.463263 2.211706-4.463263C1.39477-4.463263 .707347-3.995019 .707347-3.337484C.707347-3.038605 .9066-2.86924 1.165629-2.86924C1.444583-2.86924 1.62391-3.068493 1.62391-3.327522C1.62391-3.447073 1.574097-3.775841 1.115816-3.785803C1.384807-4.134496 1.872976-4.244085 2.191781-4.244085C2.67995-4.244085 3.247821-3.855542 3.247821-2.968867V-2.600249C2.739726-2.570361 2.042341-2.540473 1.414695-2.241594C.667497-1.902864 .418431-1.384807 .418431-.946451C.418431-.139477 1.384807 .109589 2.012453 .109589C2.669988 .109589 3.128269-.288917 3.317559-.757161ZM3.247821-2.391034V-1.39477C3.247821-.448319 2.530511-.109589 2.082192-.109589C1.594022-.109589 1.185554-.458281 1.185554-.956413C1.185554-1.504359 1.603985-2.331258 3.247821-2.391034Z'/>\n\x3Cpath id='g1-99' d='M1.165629-2.171856C1.165629-3.795766 1.982565-4.214197 2.510585-4.214197C2.600249-4.214197 3.227895-4.204234 3.576588-3.845579C3.16812-3.815691 3.108344-3.516812 3.108344-3.387298C3.108344-3.128269 3.287671-2.929016 3.566625-2.929016C3.825654-2.929016 4.024907-3.098381 4.024907-3.39726C4.024907-4.07472 3.267746-4.463263 2.500623-4.463263C1.255293-4.463263 .33873-3.387298 .33873-2.15193C.33873-.876712 1.325031 .109589 2.480697 .109589C3.815691 .109589 4.134496-1.085928 4.134496-1.185554S4.034869-1.285181 4.004981-1.285181C3.915318-1.285181 3.895392-1.24533 3.875467-1.185554C3.58655-.259029 2.938979-.139477 2.570361-.139477C2.042341-.139477 1.165629-.56787 1.165629-2.171856Z'/>\n\x3Cpath id='g1-101' d='M1.115816-2.510585C1.175592-3.995019 2.012453-4.244085 2.351183-4.244085C3.377335-4.244085 3.476961-2.899128 3.476961-2.510585H1.115816ZM1.105853-2.30137H3.88543C4.104608-2.30137 4.134496-2.30137 4.134496-2.510585C4.134496-3.496887 3.596513-4.463263 2.351183-4.463263C1.195517-4.463263 .278954-3.437111 .278954-2.191781C.278954-.856787 1.325031 .109589 2.470735 .109589C3.686177 .109589 4.134496-.996264 4.134496-1.185554C4.134496-1.285181 4.054795-1.305106 4.004981-1.305106C3.915318-1.305106 3.895392-1.24533 3.875467-1.165629C3.526775-.139477 2.630137-.139477 2.530511-.139477C2.032379-.139477 1.633873-.438356 1.404732-.806974C1.105853-1.285181 1.105853-1.942715 1.105853-2.30137Z'/>\n\x3Cpath id='g1-105' d='M1.763387-4.403487L.368618-4.293898V-3.985056C1.016189-3.985056 1.105853-3.92528 1.105853-3.437111V-.757161C1.105853-.308842 .996264-.308842 .328767-.308842V0C.647572-.009963 1.185554-.029888 1.424658-.029888C1.77335-.029888 2.122042-.009963 2.460772 0V-.308842C1.803238-.308842 1.763387-.358655 1.763387-.747198V-4.403487ZM1.803238-6.136986C1.803238-6.455791 1.554172-6.665006 1.275218-6.665006C.966376-6.665006 .747198-6.396015 .747198-6.136986C.747198-5.867995 .966376-5.608966 1.275218-5.608966C1.554172-5.608966 1.803238-5.818182 1.803238-6.136986Z'/>\n\x3Cpath id='g1-106' d='M2.092154-4.403487L.577833-4.293898V-3.985056C1.344956-3.985056 1.43462-3.915318 1.43462-3.427148V.518057C1.43462 .966376 1.344956 1.823163 .707347 1.823163C.657534 1.823163 .428394 1.823163 .169365 1.693649C.318804 1.653798 .518057 1.514321 .518057 1.24533C.518057 .986301 .33873 .787049 .059776 .787049S-.398506 .986301-.398506 1.24533C-.398506 1.763387 .159402 2.042341 .727273 2.042341C1.474471 2.042341 2.092154 1.404732 2.092154 .498132V-4.403487ZM2.092154-6.136986C2.092154-6.425903 1.853051-6.665006 1.564134-6.665006S1.036115-6.425903 1.036115-6.136986S1.275218-5.608966 1.564134-5.608966S2.092154-5.84807 2.092154-6.136986Z'/>\n\x3Cpath id='g1-110' d='M1.09589-3.427148V-.757161C1.09589-.308842 .986301-.308842 .318804-.308842V0C.667497-.009963 1.175592-.029888 1.444583-.029888C1.703611-.029888 2.221669-.009963 2.560399 0V-.308842C1.892902-.308842 1.783313-.308842 1.783313-.757161V-2.590286C1.783313-3.626401 2.49066-4.184309 3.128269-4.184309C3.755915-4.184309 3.865504-3.646326 3.865504-3.078456V-.757161C3.865504-.308842 3.755915-.308842 3.088418-.308842V0C3.437111-.009963 3.945205-.029888 4.214197-.029888C4.473225-.029888 4.991283-.009963 5.330012 0V-.308842C4.811955-.308842 4.562889-.308842 4.552927-.607721V-2.510585C4.552927-3.367372 4.552927-3.676214 4.244085-4.034869C4.104608-4.204234 3.775841-4.403487 3.198007-4.403487C2.470735-4.403487 2.002491-3.975093 1.723537-3.35741V-4.403487L.318804-4.293898V-3.985056C1.016189-3.985056 1.09589-3.915318 1.09589-3.427148Z'/>\n\x3Cpath id='g1-111' d='M4.692403-2.132005C4.692403-3.407223 3.696139-4.463263 2.49066-4.463263C1.24533-4.463263 .278954-3.377335 .278954-2.132005C.278954-.846824 1.315068 .109589 2.480697 .109589C3.686177 .109589 4.692403-.86675 4.692403-2.132005ZM2.49066-.139477C2.062267-.139477 1.62391-.348692 1.354919-.806974C1.105853-1.24533 1.105853-1.853051 1.105853-2.211706C1.105853-2.600249 1.105853-3.138232 1.344956-3.576588C1.613948-4.034869 2.082192-4.244085 2.480697-4.244085C2.919054-4.244085 3.347447-4.024907 3.606476-3.596513S3.865504-2.590286 3.865504-2.211706C3.865504-1.853051 3.865504-1.315068 3.646326-.876712C3.427148-.428394 2.988792-.139477 2.49066-.139477Z'/>\n\x3Cpath id='g1-112' d='M1.713574-3.745953V-4.403487L.278954-4.293898V-3.985056C.986301-3.985056 1.05604-3.92528 1.05604-3.486924V1.175592C1.05604 1.62391 .946451 1.62391 .278954 1.62391V1.932752C.617684 1.92279 1.135741 1.902864 1.39477 1.902864C1.663761 1.902864 2.171856 1.92279 2.520548 1.932752V1.62391C1.853051 1.62391 1.743462 1.62391 1.743462 1.175592V-.498132V-.587796C1.793275-.428394 2.211706 .109589 2.968867 .109589C4.154421 .109589 5.190535-.86675 5.190535-2.15193C5.190535-3.417186 4.224159-4.403487 3.108344-4.403487C2.331258-4.403487 1.912827-3.965131 1.713574-3.745953ZM1.743462-1.135741V-3.35741C2.032379-3.865504 2.520548-4.154421 3.028643-4.154421C3.755915-4.154421 4.363636-3.277709 4.363636-2.15193C4.363636-.946451 3.666252-.109589 2.929016-.109589C2.530511-.109589 2.15193-.308842 1.882939-.71731C1.743462-.926526 1.743462-.936488 1.743462-1.135741Z'/>\n\x3Cpath id='g1-114' d='M1.663761-3.307597V-4.403487L.278954-4.293898V-3.985056C.976339-3.985056 1.05604-3.915318 1.05604-3.427148V-.757161C1.05604-.308842 .946451-.308842 .278954-.308842V0C.667497-.009963 1.135741-.029888 1.414695-.029888C1.8132-.029888 2.281445-.029888 2.67995 0V-.308842H2.470735C1.733499-.308842 1.713574-.418431 1.713574-.777086V-2.311333C1.713574-3.297634 2.132005-4.184309 2.889166-4.184309C2.958904-4.184309 2.978829-4.184309 2.998755-4.174346C2.968867-4.164384 2.769614-4.044832 2.769614-3.785803C2.769614-3.506849 2.978829-3.35741 3.198007-3.35741C3.377335-3.35741 3.626401-3.476961 3.626401-3.795766S3.317559-4.403487 2.889166-4.403487C2.161893-4.403487 1.803238-3.73599 1.663761-3.307597Z'/>\n\x3Cpath id='g1-117' d='M3.895392-.787049V.109589L5.330012 0V-.308842C4.632628-.308842 4.552927-.37858 4.552927-.86675V-4.403487L3.088418-4.293898V-3.985056C3.785803-3.985056 3.865504-3.915318 3.865504-3.427148V-1.653798C3.865504-.787049 3.387298-.109589 2.660025-.109589C1.823163-.109589 1.783313-.577833 1.783313-1.09589V-4.403487L.318804-4.293898V-3.985056C1.09589-3.985056 1.09589-3.955168 1.09589-3.068493V-1.574097C1.09589-.797011 1.09589 .109589 2.610212 .109589C3.16812 .109589 3.606476-.169365 3.895392-.787049Z'/>\n\x3Cpath id='g0-80' d='M2.879203-3.008717H4.64259C6.027397-3.008717 7.183064-3.726027 7.183064-4.891656C7.183064-5.987547 6.196762-6.834371 4.542964-6.834371H.388543V-6.366127H1.464508V-.468244H.388543V0C.767123-.029888 1.743462-.029888 2.171856-.029888S3.576588-.029888 3.955168 0V-.468244H2.879203V-3.008717ZM4.154421-3.417186H2.819427V-6.366127H4.164384C5.65878-6.366127 5.65878-5.608966 5.65878-4.891656C5.65878-4.184309 5.65878-3.417186 4.154421-3.417186Z'/>\n\x3Cpath id='g0-82' d='M2.819427-3.596513V-6.366127H3.995019C5.599004-6.366127 5.618929-5.589041 5.618929-4.98132C5.618929-4.423412 5.618929-3.596513 3.975093-3.596513H2.819427ZM5.479452-3.387298C6.635118-3.686177 7.143213-4.333748 7.143213-4.991283C7.143213-5.997509 6.047323-6.834371 4.174346-6.834371H.388543V-6.366127H1.464508V-.468244H.388543V0C.747198-.029888 1.723537-.029888 2.141968-.029888S3.536737-.029888 3.895392 0V-.468244H2.819427V-3.237858H3.985056C4.124533-3.237858 4.562889-3.237858 4.871731-2.899128C5.190535-2.550436 5.190535-2.361146 5.190535-1.633873C5.190535-.976339 5.190535-.488169 5.88792-.14944C6.326276 .069738 6.94396 .109589 7.352428 .109589C8.418431 .109589 8.547945-.787049 8.547945-.946451C8.547945-1.165629 8.408468-1.165629 8.308842-1.165629C8.099626-1.165629 8.089664-1.066002 8.079701-.936488C8.029888-.468244 7.740971-.249066 7.442092-.249066C6.844334-.249066 6.75467-.956413 6.704857-1.374844C6.684932-1.484433 6.60523-2.171856 6.595268-2.221669C6.455791-2.919054 5.907846-3.227895 5.479452-3.387298Z'/>\n\x3Cpath id='g0-83' d='M4.004981-4.124533L2.560399-4.433375C2.161893-4.523039 1.603985-4.861768 1.603985-5.469489C1.603985-5.897883 1.882939-6.515567 2.879203-6.515567C3.676214-6.515567 4.682441-6.176837 4.911582-4.821918C4.951432-4.582814 4.951432-4.562889 5.160648-4.562889C5.399751-4.562889 5.399751-4.612702 5.399751-4.841843V-6.665006C5.399751-6.854296 5.399751-6.94396 5.220423-6.94396C5.140722-6.94396 5.13076-6.933998 5.021171-6.834371L4.572852-6.396015C3.995019-6.854296 3.347447-6.94396 2.86924-6.94396C1.354919-6.94396 .637609-5.987547 .637609-4.941469C.637609-4.293898 .966376-3.835616 1.175592-3.616438C1.663761-3.128269 2.002491-3.058531 3.088418-2.819427C3.965131-2.630137 4.134496-2.600249 4.353674-2.391034C4.503113-2.241594 4.752179-1.982565 4.752179-1.524284C4.752179-1.046077 4.493151-.358655 3.457036-.358655C2.699875-.358655 1.185554-.557908 1.105853-2.042341C1.09589-2.221669 1.09589-2.271482 .876712-2.271482C.637609-2.271482 .637609-2.211706 .637609-1.982565V-.169365C.637609 .019925 .637609 .109589 .816936 .109589C.9066 .109589 .926526 .089664 1.006227 .019925L1.464508-.438356C2.122042 .049813 3.048568 .109589 3.457036 .109589C5.100872 .109589 5.718555-1.016189 5.718555-2.022416C5.718555-3.128269 4.911582-3.935243 4.004981-4.124533Z'/>\n\x3Cpath id='g0-84' d='M7.332503-6.724782H.627646L.418431-4.323786H.886675C.976339-5.449564 1.075965-6.256538 2.510585-6.256538H3.277709V-.468244H1.753425V0C2.291407-.029888 3.39726-.029888 3.985056-.029888S5.678705-.029888 6.216687 0V-.468244H4.692403V-6.256538H5.449564C6.874222-6.256538 6.973848-5.459527 7.073474-4.323786H7.541719L7.332503-6.724782Z'/>\n\x3Cpath id='g0-97' d='M3.726027-.767123C3.726027-.458281 3.726027 0 4.762142 0H5.240349C5.439601 0 5.559153 0 5.559153-.239103C5.559153-.468244 5.429639-.468244 5.300125-.468244C4.692403-.478207 4.692403-.607721 4.692403-.836862V-2.978829C4.692403-3.865504 3.985056-4.513076 2.500623-4.513076C1.932752-4.513076 .71731-4.473225 .71731-3.596513C.71731-3.158157 1.066002-2.968867 1.334994-2.968867C1.643836-2.968867 1.96264-3.178082 1.96264-3.596513C1.96264-3.895392 1.77335-4.064757 1.743462-4.084682C2.022416-4.144458 2.34122-4.154421 2.460772-4.154421C3.20797-4.154421 3.556663-3.73599 3.556663-2.978829V-2.6401C2.849315-2.610212 .318804-2.520548 .318804-1.075965C.318804-.119552 1.554172 .059776 2.241594 .059776C3.038605 .059776 3.506849-.348692 3.726027-.767123ZM3.556663-2.331258V-1.384807C3.556663-.428394 2.6401-.298879 2.391034-.298879C1.882939-.298879 1.484433-.647572 1.484433-1.085928C1.484433-2.161893 3.058531-2.30137 3.556663-2.331258Z'/>\n\x3Cpath id='g0-101' d='M4.60274-2.171856C4.821918-2.171856 4.921544-2.171856 4.921544-2.440847C4.921544-2.749689 4.861768-3.476961 4.363636-3.975093C3.995019-4.333748 3.466999-4.513076 2.779577-4.513076C1.185554-4.513076 .318804-3.486924 .318804-2.241594C.318804-.9066 1.315068 .059776 2.919054 .059776C4.493151 .059776 4.921544-.996264 4.921544-1.165629C4.921544-1.344956 4.732254-1.344956 4.682441-1.344956C4.513076-1.344956 4.493151-1.295143 4.433375-1.135741C4.224159-.657534 3.656289-.33873 3.008717-.33873C1.603985-.33873 1.594022-1.663761 1.594022-2.171856H4.60274ZM1.594022-2.500623C1.613948-2.889166 1.62391-3.307597 1.833126-3.636364C2.092154-4.034869 2.49066-4.154421 2.779577-4.154421C3.945205-4.154421 3.965131-2.849315 3.975093-2.500623H1.594022Z'/>\n\x3Cpath id='g0-105' d='M2.231631-4.483188L.498132-4.403487V-3.935243C1.085928-3.935243 1.155666-3.935243 1.155666-3.5467V-.468244H.468244V0C.777086-.009963 1.265255-.029888 1.683686-.029888C1.982565-.029888 2.49066-.009963 2.849315 0V-.468244H2.231631V-4.483188ZM2.331258-6.146949C2.331258-6.585305 1.972603-6.924035 1.554172-6.924035C1.125778-6.924035 .777086-6.575342 .777086-6.146949S1.125778-5.369863 1.554172-5.369863C1.972603-5.369863 2.331258-5.708593 2.331258-6.146949Z'/>\n\x3Cpath id='g0-108' d='M2.231631-6.914072L.468244-6.834371V-6.366127C1.085928-6.366127 1.155666-6.366127 1.155666-5.977584V-.468244H.468244V0C.787049-.009963 1.265255-.029888 1.693649-.029888S2.580324-.009963 2.919054 0V-.468244H2.231631V-6.914072Z'/>\n\x3Cpath id='g0-109' d='M1.135741-3.5467V-.468244H.448319V0C.727273-.009963 1.325031-.029888 1.703611-.029888C2.092154-.029888 2.67995-.009963 2.958904 0V-.468244H2.271482V-2.550436C2.271482-3.636364 3.138232-4.124533 3.755915-4.124533C4.094645-4.124533 4.313823-3.92528 4.313823-3.158157V-.468244H3.626401V0C3.905355-.009963 4.503113-.029888 4.881694-.029888C5.270237-.029888 5.858032-.009963 6.136986 0V-.468244H5.449564V-2.550436C5.449564-3.636364 6.316314-4.124533 6.933998-4.124533C7.272727-4.124533 7.491905-3.92528 7.491905-3.158157V-.468244H6.804483V0C7.083437-.009963 7.681196-.029888 8.059776-.029888C8.448319-.029888 9.036115-.009963 9.315068 0V-.468244H8.627646V-3.048568C8.627646-4.07472 8.119552-4.483188 7.0934-4.483188C6.1868-4.483188 5.668742-3.985056 5.409714-3.526775C5.210461-4.4533 4.293898-4.483188 3.915318-4.483188C3.048568-4.483188 2.480697-4.034869 2.161893-3.407223V-4.483188L.448319-4.403487V-3.935243C1.066002-3.935243 1.135741-3.935243 1.135741-3.5467Z'/>\n\x3Cpath id='g0-110' d='M1.135741-3.5467V-.468244H.448319V0C.727273-.009963 1.325031-.029888 1.703611-.029888C2.092154-.029888 2.67995-.009963 2.958904 0V-.468244H2.271482V-2.550436C2.271482-3.636364 3.128269-4.124533 3.755915-4.124533C4.094645-4.124533 4.303861-3.915318 4.303861-3.158157V-.468244H3.616438V0C3.895392-.009963 4.493151-.029888 4.871731-.029888C5.260274-.029888 5.84807-.009963 6.127024 0V-.468244H5.439601V-3.048568C5.439601-4.094645 4.901619-4.483188 3.905355-4.483188C2.948941-4.483188 2.420922-3.915318 2.161893-3.407223V-4.483188L.448319-4.403487V-3.935243C1.066002-3.935243 1.135741-3.935243 1.135741-3.5467Z'/>\n\x3Cpath id='g0-111' d='M5.399751-2.171856C5.399751-3.506849 4.483188-4.513076 2.859278-4.513076C1.225405-4.513076 .318804-3.496887 .318804-2.171856C.318804-.936488 1.195517 .059776 2.859278 .059776C4.533001 .059776 5.399751-.946451 5.399751-2.171856ZM2.859278-.33873C1.594022-.33873 1.594022-1.414695 1.594022-2.281445C1.594022-2.729763 1.594022-3.237858 1.763387-3.576588C1.952677-3.945205 2.371108-4.154421 2.859278-4.154421C3.277709-4.154421 3.696139-3.995019 3.915318-3.646326C4.124533-3.307597 4.124533-2.759651 4.124533-2.281445C4.124533-1.414695 4.124533-.33873 2.859278-.33873Z'/>\n\x3Cpath id='g0-112' d='M2.191781-3.277709C2.191781-3.466999 2.201743-3.476961 2.34122-3.616438C2.739726-4.024907 3.257783-4.084682 3.476961-4.084682C4.144458-4.084682 4.702366-3.476961 4.702366-2.221669C4.702366-.816936 4.004981-.298879 3.35741-.298879C3.217933-.298879 2.749689-.298879 2.30137-.836862C2.191781-.966376 2.191781-.976339 2.191781-1.165629V-3.277709ZM2.191781-.388543C2.620174-.039851 3.058531 .059776 3.466999 .059776C4.961395 .059776 5.977584-.836862 5.977584-2.221669C5.977584-3.5467 5.070984-4.483188 3.636364-4.483188C2.889166-4.483188 2.361146-4.174346 2.132005-3.995019V-4.483188L.368618-4.403487V-3.935243C.986301-3.935243 1.05604-3.935243 1.05604-3.556663V1.464508H.368618V1.932752C.647572 1.92279 1.24533 1.902864 1.62391 1.902864C2.012453 1.902864 2.600249 1.92279 2.879203 1.932752V1.464508H2.191781V-.388543Z'/>\n\x3Cpath id='g0-114' d='M2.022416-3.35741V-4.483188L.368618-4.403487V-3.935243C.986301-3.935243 1.05604-3.935243 1.05604-3.5467V-.468244H.368618V0C.71731-.009963 1.165629-.029888 1.62391-.029888C2.002491-.029888 2.6401-.029888 2.998755 0V-.468244H2.132005V-2.211706C2.132005-2.909091 2.381071-4.124533 3.377335-4.124533C3.367372-4.11457 3.188045-3.955168 3.188045-3.666252C3.188045-3.257783 3.506849-3.058531 3.795766-3.058531S4.403487-3.267746 4.403487-3.666252C4.403487-4.194271 3.865504-4.483188 3.347447-4.483188C2.650062-4.483188 2.251557-3.985056 2.022416-3.35741Z'/>\n\x3Cpath id='g0-115' d='M2.102117-2.929016C1.733499-2.998755 1.085928-3.108344 1.085928-3.576588C1.085928-4.194271 2.012453-4.194271 2.201743-4.194271C2.948941-4.194271 3.327522-3.905355 3.377335-3.35741C3.387298-3.20797 3.39726-3.158157 3.606476-3.158157C3.845579-3.158157 3.845579-3.20797 3.845579-3.437111V-4.234122C3.845579-4.423412 3.845579-4.513076 3.666252-4.513076C3.626401-4.513076 3.606476-4.513076 3.217933-4.323786C2.958904-4.4533 2.610212-4.513076 2.211706-4.513076C1.912827-4.513076 .37858-4.513076 .37858-3.20797C.37858-2.809465 .577833-2.540473 .777086-2.371108C1.175592-2.022416 1.554172-1.96264 2.321295-1.823163C2.67995-1.763387 3.427148-1.633873 3.427148-1.046077C3.427148-.298879 2.510585-.298879 2.291407-.298879C1.235367-.298879 .976339-1.026152 .856787-1.454545C.806974-1.594022 .757161-1.594022 .617684-1.594022C.37858-1.594022 .37858-1.534247 .37858-1.305106V-.219178C.37858-.029888 .37858 .059776 .557908 .059776C.627646 .059776 .647572 .059776 .856787-.089664C.86675-.089664 1.085928-.239103 1.115816-.259029C1.574097 .059776 2.092154 .059776 2.291407 .059776C2.600249 .059776 4.134496 .059776 4.134496-1.39477C4.134496-1.823163 3.935243-2.171856 3.58655-2.450809C3.198007-2.739726 2.879203-2.799502 2.102117-2.929016Z'/>\n\x3Cpath id='g0-116' d='M1.026152-3.955168V-1.225405C1.026152-.159402 1.892902 .059776 2.600249 .059776C3.35741 .059776 3.805729-.508095 3.805729-1.235367V-1.763387H3.337484V-1.255293C3.337484-.577833 3.01868-.33873 2.739726-.33873C2.161893-.33873 2.161893-.976339 2.161893-1.205479V-3.955168H3.616438V-4.423412H2.161893V-6.326276H1.693649C1.683686-5.330012 1.195517-4.343711 .209215-4.313823V-3.955168H1.026152Z'/>\n\x3Cpath id='g0-117' d='M4.363636-.71731V.059776L6.127024 0V-.468244C5.50934-.468244 5.439601-.468244 5.439601-.856787V-4.483188L3.616438-4.403487V-3.935243C4.234122-3.935243 4.303861-3.935243 4.303861-3.5467V-1.643836C4.303861-.826899 3.795766-.298879 3.068493-.298879C2.30137-.298879 2.271482-.547945 2.271482-1.085928V-4.483188L.448319-4.403487V-3.935243C1.066002-3.935243 1.135741-3.935243 1.135741-3.5467V-1.225405C1.135741-.159402 1.942715 .059776 2.929016 .059776C3.188045 .059776 3.905355 .059776 4.363636-.71731Z'/>\n\x3Cpath id='g0-118' d='M5.041096-3.745953C5.100872-3.88543 5.140722-3.955168 5.778331-3.955168V-4.423412C5.529265-4.403487 5.240349-4.393524 4.991283-4.393524S4.293898-4.41345 4.084682-4.423412V-3.955168C4.273973-3.955168 4.562889-3.92528 4.562889-3.845579C4.562889-3.835616 4.552927-3.815691 4.513076-3.726027L3.35741-1.235367L2.092154-3.955168H2.630137V-4.423412C2.30137-4.403487 1.404732-4.393524 1.39477-4.393524C1.115816-4.393524 .667497-4.41345 .259029-4.423412V-3.955168H.896638L2.6401-.209215C2.759651 .039851 2.889166 .039851 3.01868 .039851C3.188045 .039851 3.287671 .009963 3.387298-.199253L5.041096-3.745953Z'/>\n\x3C/defs>\n\x3Cg id='page1'>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 92.3807 57.668C 92.3807 32.3657 71.8691 11.8541 46.5667 11.8541C 21.2644 11.8541 0.752812 32.3657 0.752812 57.668C 0.752812 82.9703 21.2644 103.482 46.5667 103.482C 71.8691 103.482 92.3807 82.9703 92.3807 57.668Z' fill='#f3f3f3' opacity='0.1'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 92.3807 57.668C 92.3807 32.3657 71.8691 11.8541 46.5667 11.8541C 21.2644 11.8541 0.752812 32.3657 0.752812 57.668C 0.752812 82.9703 21.2644 103.482 46.5667 103.482C 71.8691 103.482 92.3807 82.9703 92.3807 57.668Z' fill='none' stroke='#000000' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1.50562'/>\n\x3C/g>\n\x3Cuse x='107.086144' y='119.465563' xlink:href='#g0-84'/>\n\x3Cuse x='114.101462' y='119.465563' xlink:href='#g0-111'/>\n\x3Cuse x='119.82995' y='119.465563' xlink:href='#g0-116'/>\n\x3Cuse x='124.28544' y='119.465563' xlink:href='#g0-97'/>\n\x3Cuse x='129.854794' y='119.465563' xlink:href='#g0-108'/>\n\x3Cuse x='136.856279' y='119.465563' xlink:href='#g0-80'/>\n\x3Cuse x='144.369731' y='119.465563' xlink:href='#g0-111'/>\n\x3Cuse x='150.098219' y='119.465563' xlink:href='#g0-112'/>\n\x3Cuse x='156.463205' y='119.465563' xlink:href='#g0-117'/>\n\x3Cuse x='162.828191' y='119.465563' xlink:href='#g0-108'/>\n\x3Cuse x='166.010684' y='119.465563' xlink:href='#g0-97'/>\n\x3Cuse x='171.580038' y='119.465563' xlink:href='#g0-116'/>\n\x3Cuse x='176.035528' y='119.465563' xlink:href='#g0-105'/>\n\x3Cuse x='179.218021' y='119.465563' xlink:href='#g0-111'/>\n\x3Cuse x='184.946509' y='119.465563' xlink:href='#g0-110'/>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 28.91 44.0321C 28.91 43.3574 28.363 42.8104 27.6883 42.8104C 27.0136 42.8104 26.4666 43.3574 26.4666 44.0321C 26.4666 44.7068 27.0136 45.2538 27.6883 45.2538C 28.363 45.2538 28.91 44.7068 28.91 44.0321Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 17.7062 69.5206C 17.7062 68.8459 17.1592 68.2989 16.4845 68.2989C 15.8098 68.2989 15.2628 68.8459 15.2628 69.5206C 15.2628 70.1954 15.8098 70.7423 16.4845 70.7423C 17.1592 70.7423 17.7062 70.1954 17.7062 69.5206Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 55.5426 29.1898C 55.5426 28.5151 54.9956 27.9681 54.3209 27.9681C 53.6461 27.9681 53.0992 28.5151 53.0992 29.1898C 53.0992 29.8645 53.6461 30.4115 54.3209 30.4115C 54.9956 30.4115 55.5426 29.8645 55.5426 29.1898Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 45.3201 63.7814C 45.3201 63.1066 44.7731 62.5597 44.0984 62.5597C 43.4236 62.5597 42.8767 63.1066 42.8767 63.7814C 42.8767 64.4561 43.4236 65.0031 44.0984 65.0031C 44.7731 65.0031 45.3201 64.4561 45.3201 63.7814Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 54.8843 31.9505C 54.8843 31.2757 54.3374 30.7288 53.6626 30.7288C 52.9879 30.7288 52.4409 31.2757 52.4409 31.9505C 52.4409 32.6252 52.9879 33.1722 53.6626 33.1722C 54.3374 33.1722 54.8843 32.6252 54.8843 31.9505Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 50.2222 57.4683C 50.2222 56.7936 49.6752 56.2466 49.0005 56.2466C 48.3258 56.2466 47.7788 56.7936 47.7788 57.4683C 47.7788 58.143 48.3258 58.69 49.0005 58.69C 49.6752 58.69 50.2222 58.143 50.2222 57.4683Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 28.9738 74.5074C 28.9738 73.8327 28.4268 73.2857 27.7521 73.2857C 27.0773 73.2857 26.5303 73.8327 26.5303 74.5074C 26.5303 75.1821 27.0773 75.7291 27.7521 75.7291C 28.4268 75.7291 28.9738 75.1821 28.9738 74.5074Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 16.6062 38.0535C 16.6062 37.3788 16.0592 36.8318 15.3845 36.8318C 14.7098 36.8318 14.1628 37.3788 14.1628 38.0535C 14.1628 38.7283 14.7098 39.2752 15.3845 39.2752C 16.0592 39.2752 16.6062 38.7283 16.6062 38.0535Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 67.3945 54.344C 67.3945 53.6693 66.8475 53.1223 66.1728 53.1223C 65.4981 53.1223 64.9511 53.6693 64.9511 54.344C 64.9511 55.0187 65.4981 55.5657 66.1728 55.5657C 66.8475 55.5657 67.3945 55.0187 67.3945 54.344Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 39.2264 48.161C 39.2264 47.4863 38.6795 46.9393 38.0047 46.9393C 37.33 46.9393 36.783 47.4863 36.783 48.161C 36.783 48.8357 37.33 49.3827 38.0047 49.3827C 38.6795 49.3827 39.2264 48.8357 39.2264 48.161Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 50.0463 58.5604C 50.0463 57.8857 49.4993 57.3387 48.8246 57.3387C 48.1498 57.3387 47.6029 57.8857 47.6029 58.5604C 47.6029 59.2352 48.1498 59.7821 48.8246 59.7821C 49.4993 59.7821 50.0463 59.2352 50.0463 58.5604Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 76.0292 73.5033C 76.0292 72.8286 75.4822 72.2816 74.8075 72.2816C 74.1328 72.2816 73.5858 72.8286 73.5858 73.5033C 73.5858 74.178 74.1328 74.725 74.8075 74.725C 75.4822 74.725 76.0292 74.178 76.0292 73.5033Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 56.1995 81.4538C 56.1995 80.7791 55.6525 80.2321 54.9778 80.2321C 54.303 80.2321 53.7561 80.7791 53.7561 81.4538C 53.7561 82.1286 54.303 82.6755 54.9778 82.6755C 55.6525 82.6755 56.1995 82.1286 56.1995 81.4538Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 77.9112 57.0154C 77.9112 56.3407 77.3642 55.7937 76.6895 55.7937C 76.0147 55.7937 75.4677 56.3407 75.4677 57.0154C 75.4677 57.6901 76.0147 58.2371 76.6895 58.2371C 77.3642 58.2371 77.9112 57.6901 77.9112 57.0154Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 67.8816 76.0197C 67.8816 75.3449 67.3346 74.798 66.6599 74.798C 65.9852 74.798 65.4382 75.3449 65.4382 76.0197C 65.4382 76.6944 65.9852 77.2414 66.6599 77.2414C 67.3346 77.2414 67.8816 76.6944 67.8816 76.0197Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 85.2764 61.0486C 85.2764 60.3739 84.7295 59.8269 84.0547 59.8269C 83.38 59.8269 82.833 60.3739 82.833 61.0486C 82.833 61.7233 83.38 62.2703 84.0547 62.2703C 84.7295 62.2703 85.2764 61.7233 85.2764 61.0486Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 65.5266 39.2454C 65.5266 38.5707 64.9796 38.0237 64.3049 38.0237C 63.6301 38.0237 63.0832 38.5707 63.0832 39.2454C 63.0832 39.9201 63.6301 40.4671 64.3049 40.4671C 64.9796 40.4671 65.5266 39.9201 65.5266 39.2454Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 52.6907 93.6613C 52.6907 92.9866 52.1438 92.4396 51.469 92.4396C 50.7943 92.4396 50.2473 92.9866 50.2473 93.6613C 50.2473 94.336 50.7943 94.883 51.469 94.883C 52.1438 94.883 52.6907 94.336 52.6907 93.6613Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 27.6913 83.3176C 27.6913 82.6428 27.1443 82.0959 26.4696 82.0959C 25.7948 82.0959 25.2479 82.6428 25.2479 83.3176C 25.2479 83.9923 25.7948 84.5393 26.4696 84.5393C 27.1443 84.5393 27.6913 83.9923 27.6913 83.3176Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 78.8353 58.0928C 78.8353 57.4181 78.2883 56.8711 77.6136 56.8711C 76.9388 56.8711 76.3919 57.4181 76.3919 58.0928C 76.3919 58.7675 76.9388 59.3145 77.6136 59.3145C 78.2883 59.3145 78.8353 58.7675 78.8353 58.0928Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 66.1232 40.2523C 66.1232 39.5776 65.5763 39.0306 64.9015 39.0306C 64.2268 39.0306 63.6798 39.5776 63.6798 40.2523C 63.6798 40.927 64.2268 41.474 64.9015 41.474C 65.5763 41.474 66.1232 40.927 66.1232 40.2523Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 35.9831 51.6183C 35.9831 50.9436 35.4361 50.3966 34.7614 50.3966C 34.0866 50.3966 33.5397 50.9436 33.5397 51.6183C 33.5397 52.293 34.0866 52.84 34.7614 52.84C 35.4361 52.84 35.9831 52.293 35.9831 51.6183Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 60.6896 47.9543C 60.6896 47.2795 60.1426 46.7326 59.4679 46.7326C 58.7931 46.7326 58.2462 47.2795 58.2462 47.9543C 58.2462 48.629 58.7931 49.176 59.4679 49.176C 60.1426 49.176 60.6896 48.629 60.6896 47.9543Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 66.6164 60.5518C 66.6164 59.877 66.0694 59.3301 65.3947 59.3301C 64.7199 59.3301 64.173 59.877 64.173 60.5518C 64.173 61.2265 64.7199 61.7735 65.3947 61.7735C 66.0694 61.7735 66.6164 61.2265 66.6164 60.5518Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 34.5264 76.4368C 34.5264 75.7621 33.9794 75.2151 33.3047 75.2151C 32.63 75.2151 32.083 75.7621 32.083 76.4368C 32.083 77.1115 32.63 77.6585 33.3047 77.6585C 33.9794 77.6585 34.5264 77.1115 34.5264 76.4368Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 29.6881 35.8315C 29.6881 35.1568 29.1411 34.6098 28.4664 34.6098C 27.7917 34.6098 27.2447 35.1568 27.2447 35.8315C 27.2447 36.5063 27.7917 37.0533 28.4664 37.0533C 29.1411 37.0533 29.6881 36.5063 29.6881 35.8315Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 79.6012 39.7852C 79.6012 39.1105 79.0542 38.5635 78.3795 38.5635C 77.7047 38.5635 77.1578 39.1105 77.1578 39.7852C 77.1578 40.4599 77.7047 41.0069 78.3795 41.0069C 79.0542 41.0069 79.6012 40.4599 79.6012 39.7852Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 32.6996 34.7824C 32.6996 34.1077 32.1526 33.5607 31.4779 33.5607C 30.8032 33.5607 30.2562 34.1077 30.2562 34.7824C 30.2562 35.4572 30.8032 36.0041 31.4779 36.0041C 32.1526 36.0041 32.6996 35.4572 32.6996 34.7824Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 38.8948 82.9205C 38.8948 82.2458 38.3478 81.6988 37.6731 81.6988C 36.9983 81.6988 36.4514 82.2458 36.4514 82.9205C 36.4514 83.5953 36.9983 84.1422 37.6731 84.1422C 38.3478 84.1422 38.8948 83.5953 38.8948 82.9205Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 38.9218 51.8424C 38.9218 51.1677 38.3749 50.6207 37.7001 50.6207C 37.0254 50.6207 36.4784 51.1677 36.4784 51.8424C 36.4784 52.5171 37.0254 53.0641 37.7001 53.0641C 38.3749 53.0641 38.9218 52.5171 38.9218 51.8424Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 38.7243 72.3726C 38.7243 71.6978 38.1773 71.1509 37.5026 71.1509C 36.8279 71.1509 36.2809 71.6978 36.2809 72.3726C 36.2809 73.0473 36.8279 73.5943 37.5026 73.5943C 38.1773 73.5943 38.7243 73.0473 38.7243 72.3726Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 60.3079 23.4127C 60.3079 22.7379 59.7609 22.1909 59.0862 22.1909C 58.4115 22.1909 57.8645 22.7379 57.8645 23.4127C 57.8645 24.0874 58.4115 24.6344 59.0862 24.6344C 59.7609 24.6344 60.3079 24.0874 60.3079 23.4127Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 30.4078 56.6524C 30.4078 55.9777 29.8608 55.4307 29.1861 55.4307C 28.5113 55.4307 27.9643 55.9777 27.9643 56.6524C 27.9643 57.3271 28.5113 57.8741 29.1861 57.8741C 29.8608 57.8741 30.4078 57.3271 30.4078 56.6524Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 15.7858 37.671C 15.7858 36.9963 15.2388 36.4493 14.5641 36.4493C 13.8893 36.4493 13.3424 36.9963 13.3424 37.671C 13.3424 38.3458 13.8893 38.8927 14.5641 38.8927C 15.2388 38.8927 15.7858 38.3458 15.7858 37.671Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 64.3593 27.0195C 64.3593 26.3447 63.8123 25.7978 63.1376 25.7978C 62.4629 25.7978 61.9159 26.3447 61.9159 27.0195C 61.9159 27.6942 62.4629 28.2412 63.1376 28.2412C 63.8123 28.2412 64.3593 27.6942 64.3593 27.0195Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 72.3981 67.7338C 72.3981 67.059 71.8511 66.5121 71.1764 66.5121C 70.5016 66.5121 69.9546 67.059 69.9546 67.7338C 69.9546 68.4085 70.5016 68.9555 71.1764 68.9555C 71.8511 68.9555 72.3981 68.4085 72.3981 67.7338Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 72.3691 60.1255C 72.3691 59.4508 71.8222 58.9038 71.1474 58.9038C 70.4727 58.9038 69.9257 59.4508 69.9257 60.1255C 69.9257 60.8002 70.4727 61.3472 71.1474 61.3472C 71.8222 61.3472 72.3691 60.8002 72.3691 60.1255Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 34.219 52.2813C 34.219 51.6066 33.672 51.0596 32.9973 51.0596C 32.3226 51.0596 31.7756 51.6066 31.7756 52.2813C 31.7756 52.9561 32.3226 53.503 32.9973 53.503C 33.672 53.503 34.219 52.9561 34.219 52.2813Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 31.2696 90.6102C 31.2696 89.9354 30.7226 89.3885 30.0479 89.3885C 29.3732 89.3885 28.8262 89.9354 28.8262 90.6102C 28.8262 91.2849 29.3732 91.8319 30.0479 91.8319C 30.7226 91.8319 31.2696 91.2849 31.2696 90.6102Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 15.2231 37.5354C 15.2231 36.8607 14.6761 36.3137 14.0014 36.3137C 13.3267 36.3137 12.7797 36.8607 12.7797 37.5354C 12.7797 38.2102 13.3267 38.7572 14.0014 38.7572C 14.6761 38.7572 15.2231 38.2102 15.2231 37.5354Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 45.2477 75.8683C 45.2477 75.1935 44.7007 74.6466 44.026 74.6466C 43.3513 74.6466 42.8043 75.1935 42.8043 75.8683C 42.8043 76.543 43.3513 77.09 44.026 77.09C 44.7007 77.09 45.2477 76.543 45.2477 75.8683Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 74.0962 49.278C 74.0962 48.6033 73.5492 48.0563 72.8745 48.0563C 72.1998 48.0563 71.6528 48.6033 71.6528 49.278C 71.6528 49.9527 72.1998 50.4997 72.8745 50.4997C 73.5492 50.4997 74.0962 49.9527 74.0962 49.278Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 32.724 39.4537C 32.724 38.779 32.177 38.232 31.5023 38.232C 30.8275 38.232 30.2806 38.779 30.2806 39.4537C 30.2806 40.1285 30.8275 40.6755 31.5023 40.6755C 32.177 40.6755 32.724 40.1285 32.724 39.4537Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 77.4805 73.1061C 77.4805 72.4314 76.9336 71.8844 76.2588 71.8844C 75.5841 71.8844 75.0371 72.4314 75.0371 73.1061C 75.0371 73.7808 75.5841 74.3278 76.2588 74.3278C 76.9336 74.3278 77.4805 73.7808 77.4805 73.1061Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 50.009 54.4931C 50.009 53.8184 49.462 53.2714 48.7873 53.2714C 48.1125 53.2714 47.5656 53.8184 47.5656 54.4931C 47.5656 55.1678 48.1125 55.7148 48.7873 55.7148C 49.462 55.7148 50.009 55.1678 50.009 54.4931Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 73.3669 31.4686C 73.3669 30.7939 72.8199 30.2469 72.1452 30.2469C 71.4705 30.2469 70.9235 30.7939 70.9235 31.4686C 70.9235 32.1433 71.4705 32.6903 72.1452 32.6903C 72.8199 32.6903 73.3669 32.1433 73.3669 31.4686Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 29.882 50.9376C 29.882 50.2629 29.335 49.7159 28.6603 49.7159C 27.9856 49.7159 27.4386 50.2629 27.4386 50.9376C 27.4386 51.6123 27.9856 52.1593 28.6603 52.1593C 29.335 52.1593 29.882 51.6123 29.882 50.9376Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 67.3003 88.1477C 67.3003 87.4729 66.7533 86.926 66.0786 86.926C 65.4038 86.926 64.8569 87.4729 64.8569 88.1477C 64.8569 88.8224 65.4038 89.3694 66.0786 89.3694C 66.7533 89.3694 67.3003 88.8224 67.3003 88.1477Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 63.4235 69.9736C 63.4235 69.2989 62.8766 68.7519 62.2018 68.7519C 61.5271 68.7519 60.9801 69.2989 60.9801 69.9736C 60.9801 70.6484 61.5271 71.1953 62.2018 71.1953C 62.8766 71.1953 63.4235 70.6484 63.4235 69.9736Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 77.672 37.52C 77.672 36.8452 77.125 36.2983 76.4502 36.2983C 75.7755 36.2983 75.2285 36.8452 75.2285 37.52C 75.2285 38.1947 75.7755 38.7417 76.4502 38.7417C 77.125 38.7417 77.672 38.1947 77.672 37.52Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 85.5824 65.1124C 85.5824 64.4377 85.0354 63.8907 84.3607 63.8907C 83.686 63.8907 83.139 64.4377 83.139 65.1124C 83.139 65.7872 83.686 66.3341 84.3607 66.3341C 85.0354 66.3341 85.5824 65.7872 85.5824 65.1124Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 41.2211 67.5822C 41.2211 66.9075 40.6741 66.3605 39.9994 66.3605C 39.3247 66.3605 38.7777 66.9075 38.7777 67.5822C 38.7777 68.257 39.3247 68.8039 39.9994 68.8039C 40.6741 68.8039 41.2211 68.257 41.2211 67.5822Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 82.3792 55.944C 82.3792 55.2692 81.8323 54.7222 81.1575 54.7222C 80.4828 54.7222 79.9358 55.2692 79.9358 55.944C 79.9358 56.6187 80.4828 57.1657 81.1575 57.1657C 81.8323 57.1657 82.3792 56.6187 82.3792 55.944Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 50.1859 29.2211C 50.1859 28.5463 49.6389 27.9994 48.9641 27.9994C 48.2894 27.9994 47.7424 28.5463 47.7424 29.2211C 47.7424 29.8958 48.2894 30.4428 48.9641 30.4428C 49.6389 30.4428 50.1859 29.8958 50.1859 29.2211Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 21.456 72.6478C 21.456 71.9731 20.909 71.4261 20.2343 71.4261C 19.5596 71.4261 19.0126 71.9731 19.0126 72.6478C 19.0126 73.3226 19.5596 73.8695 20.2343 73.8695C 20.909 73.8695 21.456 73.3226 21.456 72.6478Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 61.9046 53.5864C 61.9046 52.9117 61.3576 52.3647 60.6829 52.3647C 60.0081 52.3647 59.4611 52.9117 59.4611 53.5864C 59.4611 54.2611 60.0081 54.8081 60.6829 54.8081C 61.3576 54.8081 61.9046 54.2611 61.9046 53.5864Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 29.6891 31.604C 29.6891 30.9292 29.1422 30.3823 28.4674 30.3823C 27.7927 30.3823 27.2457 30.9292 27.2457 31.604C 27.2457 32.2787 27.7927 32.8257 28.4674 32.8257C 29.1422 32.8257 29.6891 32.2787 29.6891 31.604Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 23.0944 87.1568C 23.0944 86.4821 22.5474 85.9351 21.8726 85.9351C 21.1979 85.9351 20.6509 86.4821 20.6509 87.1568C 20.6509 87.8315 21.1979 88.3785 21.8726 88.3785C 22.5474 88.3785 23.0944 87.8315 23.0944 87.1568Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 82.2885 68.6675C 82.2885 67.9927 81.7415 67.4458 81.0668 67.4458C 80.3921 67.4458 79.8451 67.9927 79.8451 68.6675C 79.8451 69.3422 80.3921 69.8892 81.0668 69.8892C 81.7415 69.8892 82.2885 69.3422 82.2885 68.6675Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 65.328 58.8018C 65.328 58.1271 64.781 57.5801 64.1063 57.5801C 63.4315 57.5801 62.8845 58.1271 62.8845 58.8018C 62.8845 59.4765 63.4315 60.0235 64.1063 60.0235C 64.781 60.0235 65.328 59.4765 65.328 58.8018Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 22.5989 60.0723C 22.5989 59.3976 22.0519 58.8506 21.3772 58.8506C 20.7025 58.8506 20.1555 59.3976 20.1555 60.0723C 20.1555 60.7471 20.7025 61.294 21.3772 61.294C 22.0519 61.294 22.5989 60.7471 22.5989 60.0723Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 24.6408 69.0718C 24.6408 68.3971 24.0938 67.8501 23.4191 67.8501C 22.7444 67.8501 22.1974 68.3971 22.1974 69.0718C 22.1974 69.7465 22.7444 70.2935 23.4191 70.2935C 24.0938 70.2935 24.6408 69.7465 24.6408 69.0718Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 24.5451 49.2247C 24.5451 48.55 23.9981 48.003 23.3234 48.003C 22.6486 48.003 22.1017 48.55 22.1017 49.2247C 22.1017 49.8995 22.6486 50.4464 23.3234 50.4464C 23.9981 50.4464 24.5451 49.8995 24.5451 49.2247Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 57.4845 57.1901C 57.4845 56.5154 56.9375 55.9684 56.2628 55.9684C 55.5881 55.9684 55.0411 56.5154 55.0411 57.1901C 55.0411 57.8649 55.5881 58.4119 56.2628 58.4119C 56.9375 58.4119 57.4845 57.8649 57.4845 57.1901Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 70.1702 45.2126C 70.1702 44.5378 69.6233 43.9909 68.9485 43.9909C 68.2738 43.9909 67.7268 44.5378 67.7268 45.2126C 67.7268 45.8873 68.2738 46.4343 68.9485 46.4343C 69.6233 46.4343 70.1702 45.8873 70.1702 45.2126Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 19.6435 78.6545C 19.6435 77.9798 19.0965 77.4328 18.4218 77.4328C 17.7471 77.4328 17.2001 77.9798 17.2001 78.6545C 17.2001 79.3292 17.7471 79.8762 18.4218 79.8762C 19.0965 79.8762 19.6435 79.3292 19.6435 78.6545Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 34.1001 76.3491C 34.1001 75.6744 33.5532 75.1274 32.8784 75.1274C 32.2037 75.1274 31.6567 75.6744 31.6567 76.3491C 31.6567 77.0239 32.2037 77.5708 32.8784 77.5708C 33.5532 77.5708 34.1001 77.0239 34.1001 76.3491Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 36.1143 60.1919C 36.1143 59.5172 35.5673 58.9702 34.8926 58.9702C 34.2179 58.9702 33.6709 59.5172 33.6709 60.1919C 33.6709 60.8667 34.2179 61.4136 34.8926 61.4136C 35.5673 61.4136 36.1143 60.8667 36.1143 60.1919Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 66.5433 78.4742C 66.5433 77.7995 65.9963 77.2525 65.3216 77.2525C 64.6469 77.2525 64.0999 77.7995 64.0999 78.4742C 64.0999 79.149 64.6469 79.6959 65.3216 79.6959C 65.9963 79.6959 66.5433 79.149 66.5433 78.4742Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 73.6683 84.8754C 73.6683 84.2006 73.1213 83.6536 72.4466 83.6536C 71.7718 83.6536 71.2249 84.2006 71.2249 84.8754C 71.2249 85.5501 71.7718 86.0971 72.4466 86.0971C 73.1213 86.0971 73.6683 85.5501 73.6683 84.8754Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 32.6161 93.0527C 32.6161 92.3779 32.0691 91.8309 31.3944 91.8309C 30.7196 91.8309 30.1727 92.3779 30.1727 93.0527C 30.1727 93.7274 30.7196 94.2744 31.3944 94.2744C 32.0691 94.2744 32.6161 93.7274 32.6161 93.0527Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 31.335 79.3894C 31.335 78.7147 30.788 78.1677 30.1133 78.1677C 29.4386 78.1677 28.8916 78.7147 28.8916 79.3894C 28.8916 80.0642 29.4386 80.6111 30.1133 80.6111C 30.788 80.6111 31.335 80.0642 31.335 79.3894Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 42.7941 72.7601C 42.7941 72.0854 42.2471 71.5384 41.5724 71.5384C 40.8976 71.5384 40.3507 72.0854 40.3507 72.7601C 40.3507 73.4348 40.8976 73.9818 41.5724 73.9818C 42.2471 73.9818 42.7941 73.4348 42.7941 72.7601Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 24.1895 32.7977C 24.1895 32.123 23.6425 31.576 22.9678 31.576C 22.293 31.576 21.7461 32.123 21.7461 32.7977C 21.7461 33.4724 22.293 34.0194 22.9678 34.0194C 23.6425 34.0194 24.1895 33.4724 24.1895 32.7977Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 65.5139 42.7497C 65.5139 42.075 64.9669 41.528 64.2922 41.528C 63.6174 41.528 63.0705 42.075 63.0705 42.7497C 63.0705 43.4245 63.6174 43.9714 64.2922 43.9714C 64.9669 43.9714 65.5139 43.4245 65.5139 42.7497Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 38.4223 76.2044C 38.4223 75.5296 37.8753 74.9827 37.2006 74.9827C 36.5259 74.9827 35.9789 75.5296 35.9789 76.2044C 35.9789 76.8791 36.5259 77.4261 37.2006 77.4261C 37.8753 77.4261 38.4223 76.8791 38.4223 76.2044Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 46.3411 75.896C 46.3411 75.2213 45.7941 74.6743 45.1194 74.6743C 44.4446 74.6743 43.8976 75.2213 43.8976 75.896C 43.8976 76.5708 44.4446 77.1177 45.1194 77.1177C 45.7941 77.1177 46.3411 76.5708 46.3411 75.896Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 15.5731 68.3213C 15.5731 67.6466 15.0262 67.0996 14.3514 67.0996C 13.6767 67.0996 13.1297 67.6466 13.1297 68.3213C 13.1297 68.996 13.6767 69.543 14.3514 69.543C 15.0262 69.543 15.5731 68.996 15.5731 68.3213Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 25.1881 42.6321C 25.1881 41.9574 24.6412 41.4104 23.9664 41.4104C 23.2917 41.4104 22.7447 41.9574 22.7447 42.6321C 22.7447 43.3068 23.2917 43.8538 23.9664 43.8538C 24.6412 43.8538 25.1881 43.3068 25.1881 42.6321Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 73.945 34.6129C 73.945 33.9382 73.398 33.3912 72.7233 33.3912C 72.0485 33.3912 71.5015 33.9382 71.5015 34.6129C 71.5015 35.2877 72.0485 35.8347 72.7233 35.8347C 73.398 35.8347 73.945 35.2877 73.945 34.6129Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 74.0665 32.8418C 74.0665 32.1671 73.5195 31.6201 72.8448 31.6201C 72.1701 31.6201 71.6231 32.1671 71.6231 32.8418C 71.6231 33.5165 72.1701 34.0635 72.8448 34.0635C 73.5195 34.0635 74.0665 33.5165 74.0665 32.8418Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 15.9172 45.4924C 15.9172 44.8176 15.3702 44.2706 14.6955 44.2706C 14.0208 44.2706 13.4738 44.8176 13.4738 45.4924C 13.4738 46.1671 14.0208 46.7141 14.6955 46.7141C 15.3702 46.7141 15.9172 46.1671 15.9172 45.4924Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 61.6486 79.1479C 61.6486 78.4731 61.1016 77.9262 60.4269 77.9262C 59.7522 77.9262 59.2052 78.4731 59.2052 79.1479C 59.2052 79.8226 59.7522 80.3696 60.4269 80.3696C 61.1016 80.3696 61.6486 79.8226 61.6486 79.1479Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 56.9296 95.0961C 56.9296 94.4214 56.3826 93.8744 55.7079 93.8744C 55.0332 93.8744 54.4862 94.4214 54.4862 95.0961C 54.4862 95.7708 55.0332 96.3178 55.7079 96.3178C 56.3826 96.3178 56.9296 95.7708 56.9296 95.0961Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 52.5018 86.9228C 52.5018 86.248 51.9548 85.7011 51.2801 85.7011C 50.6054 85.7011 50.0584 86.248 50.0584 86.9228C 50.0584 87.5975 50.6054 88.1445 51.2801 88.1445C 51.9548 88.1445 52.5018 87.5975 52.5018 86.9228Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 80.6943 50.1652C 80.6943 49.4905 80.1473 48.9435 79.4726 48.9435C 78.7979 48.9435 78.2509 49.4905 78.2509 50.1652C 78.2509 50.84 78.7979 51.3869 79.4726 51.3869C 80.1473 51.3869 80.6943 50.84 80.6943 50.1652Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 39.2964 21.1794C 39.2964 20.5046 38.7494 19.9577 38.0747 19.9577C 37.4 19.9577 36.853 20.5046 36.853 21.1794C 36.853 21.8541 37.4 22.4011 38.0747 22.4011C 38.7494 22.4011 39.2964 21.8541 39.2964 21.1794Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 50.8806 28.1484C 50.8806 27.4737 50.3336 26.9267 49.6589 26.9267C 48.9842 26.9267 48.4372 27.4737 48.4372 28.1484C 48.4372 28.8232 48.9842 29.3701 49.6589 29.3701C 50.3336 29.3701 50.8806 28.8232 50.8806 28.1484Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 69.7284 55.3623C 69.7284 54.6876 69.1814 54.1406 68.5067 54.1406C 67.8319 54.1406 67.285 54.6876 67.285 55.3623C 67.285 56.0371 67.8319 56.5841 68.5067 56.5841C 69.1814 56.5841 69.7284 56.0371 69.7284 55.3623Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 56.7619 87.1789C 56.7619 86.5042 56.2149 85.9572 55.5402 85.9572C 54.8654 85.9572 54.3185 86.5042 54.3185 87.1789C 54.3185 87.8536 54.8654 88.4006 55.5402 88.4006C 56.2149 88.4006 56.7619 87.8536 56.7619 87.1789Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 31.5985 53.2937C 31.5985 52.619 31.0515 52.072 30.3768 52.072C 29.702 52.072 29.1551 52.619 29.1551 53.2937C 29.1551 53.9684 29.702 54.5154 30.3768 54.5154C 31.0515 54.5154 31.5985 53.9684 31.5985 53.2937Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 37.2943 51.7339C 37.2943 51.0592 36.7473 50.5122 36.0726 50.5122C 35.3979 50.5122 34.8509 51.0592 34.8509 51.7339C 34.8509 52.4087 35.3979 52.9556 36.0726 52.9556C 36.7473 52.9556 37.2943 52.4087 37.2943 51.7339Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 24.0371 32.6718C 24.0371 31.9971 23.4901 31.4501 22.8154 31.4501C 22.1406 31.4501 21.5937 31.9971 21.5937 32.6718C 21.5937 33.3466 22.1406 33.8935 22.8154 33.8935C 23.4901 33.8935 24.0371 33.3466 24.0371 32.6718Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 13.4081 49.2958C 13.4081 48.6211 12.8611 48.0741 12.1864 48.0741C 11.5117 48.0741 10.9647 48.6211 10.9647 49.2958C 10.9647 49.9705 11.5117 50.5175 12.1864 50.5175C 12.8611 50.5175 13.4081 49.9705 13.4081 49.2958Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 48.8837 24.0138C 48.8837 23.339 48.3367 22.792 47.662 22.792C 46.9873 22.792 46.4403 23.339 46.4403 24.0138C 46.4403 24.6885 46.9873 25.2355 47.662 25.2355C 48.3367 25.2355 48.8837 24.6885 48.8837 24.0138Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 33.77 52.508C 33.77 51.8333 33.2231 51.2863 32.5483 51.2863C 31.8736 51.2863 31.3266 51.8333 31.3266 52.508C 31.3266 53.1828 31.8736 53.7297 32.5483 53.7297C 33.2231 53.7297 33.77 53.1828 33.77 52.508Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 12.3676 55.5311C 12.3676 54.8563 11.8207 54.3094 11.1459 54.3094C 10.4712 54.3094 9.92422 54.8563 9.92422 55.5311C 9.92422 56.2058 10.4712 56.7528 11.1459 56.7528C 11.8207 56.7528 12.3676 56.2058 12.3676 55.5311Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 19.3427 43.2655C 19.3427 42.5908 18.7957 42.0438 18.1209 42.0438C 17.4462 42.0438 16.8992 42.5908 16.8992 43.2655C 16.8992 43.9403 17.4462 44.4872 18.1209 44.4872C 18.7957 44.4872 19.3427 43.9403 19.3427 43.2655Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 85.7839 58.4496C 85.7839 57.7749 85.2369 57.2279 84.5622 57.2279C 83.8874 57.2279 83.3404 57.7749 83.3404 58.4496C 83.3404 59.1243 83.8874 59.6713 84.5622 59.6713C 85.2369 59.6713 85.7839 59.1243 85.7839 58.4496Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 19.9391 68.932C 19.9391 68.2573 19.3921 67.7103 18.7174 67.7103C 18.0426 67.7103 17.4957 68.2573 17.4957 68.932C 17.4957 69.6067 18.0426 70.1537 18.7174 70.1537C 19.3921 70.1537 19.9391 69.6067 19.9391 68.932Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 191.644 57.668C 191.644 45.0168 181.388 34.7611 168.737 34.7611C 156.086 34.7611 145.83 45.0168 145.83 57.668C 145.83 70.3192 156.086 80.575 168.737 80.575C 181.388 80.575 191.644 70.3192 191.644 57.668Z' fill='#f3f3f3' opacity='0.1'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 191.644 57.668C 191.644 45.0168 181.388 34.7611 168.737 34.7611C 156.086 34.7611 145.83 45.0168 145.83 57.668C 145.83 70.3192 156.086 80.575 168.737 80.575C 181.388 80.575 191.644 70.3192 191.644 57.668Z' fill='none' stroke='#000000' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1.50562'/>\n\x3C/g>\n\x3Cuse x='213.544256' y='142.28695' xlink:href='#g0-82'/>\n\x3Cuse x='222.136988' y='142.28695' xlink:href='#g0-101'/>\n\x3Cuse x='227.388111' y='142.28695' xlink:href='#g0-112'/>\n\x3Cuse x='233.753097' y='142.28695' xlink:href='#g0-114'/>\n\x3Cuse x='238.471502' y='142.28695' xlink:href='#g0-101'/>\n\x3Cuse x='243.722625' y='142.28695' xlink:href='#g0-115'/>\n\x3Cuse x='248.241764' y='142.28695' xlink:href='#g0-101'/>\n\x3Cuse x='253.492887' y='142.28695' xlink:href='#g0-110'/>\n\x3Cuse x='259.539624' y='142.28695' xlink:href='#g0-116'/>\n\x3Cuse x='263.995114' y='142.28695' xlink:href='#g0-97'/>\n\x3Cuse x='269.564468' y='142.28695' xlink:href='#g0-116'/>\n\x3Cuse x='274.019958' y='142.28695' xlink:href='#g0-105'/>\n\x3Cuse x='277.202451' y='142.28695' xlink:href='#g0-118'/>\n\x3Cuse x='282.930939' y='142.28695' xlink:href='#g0-101'/>\n\x3Cuse x='292.001054' y='142.28695' xlink:href='#g0-83'/>\n\x3Cuse x='298.36604' y='142.28695' xlink:href='#g0-97'/>\n\x3Cuse x='303.935394' y='142.28695' xlink:href='#g0-109'/>\n\x3Cuse x='313.482873' y='142.28695' xlink:href='#g0-112'/>\n\x3Cuse x='319.847859' y='142.28695' xlink:href='#g0-108'/>\n\x3Cuse x='323.030353' y='142.28695' xlink:href='#g0-101'/>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 166.349 54.6943C 166.349 54.0195 165.802 53.4725 165.127 53.4725C 164.452 53.4725 163.905 54.0195 163.905 54.6943C 163.905 55.369 164.452 55.916 165.127 55.916C 165.802 55.916 166.349 55.369 166.349 54.6943Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 181.19 66.1021C 181.19 65.4273 180.643 64.8804 179.968 64.8804C 179.294 64.8804 178.747 65.4273 178.747 66.1021C 178.747 66.7768 179.294 67.3238 179.968 67.3238C 180.643 67.3238 181.19 66.7768 181.19 66.1021Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 165.007 47.3436C 165.007 46.6689 164.46 46.1219 163.785 46.1219C 163.11 46.1219 162.563 46.6689 162.563 47.3436C 162.563 48.0183 163.11 48.5653 163.785 48.5653C 164.46 48.5653 165.007 48.0183 165.007 47.3436Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 175.992 67.5485C 175.992 66.8738 175.445 66.3268 174.77 66.3268C 174.095 66.3268 173.548 66.8738 173.548 67.5485C 173.548 68.2232 174.095 68.7702 174.77 68.7702C 175.445 68.7702 175.992 68.2232 175.992 67.5485Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 165.709 50.0605C 165.709 49.3858 165.162 48.8388 164.487 48.8388C 163.813 48.8388 163.266 49.3858 163.266 50.0605C 163.266 50.7353 163.813 51.2823 164.487 51.2823C 165.162 51.2823 165.709 50.7353 165.709 50.0605Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 175.022 55.2319C 175.022 54.5572 174.475 54.0102 173.8 54.0102C 173.125 54.0102 172.578 54.5572 172.578 55.2319C 172.578 55.9067 173.125 56.4537 173.8 56.4537C 174.475 56.4537 175.022 55.9067 175.022 55.2319Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 166.47 55.8333C 166.47 55.1586 165.923 54.6116 165.248 54.6116C 164.573 54.6116 164.026 55.1586 164.026 55.8333C 164.026 56.508 164.573 57.055 165.248 57.055C 165.923 57.055 166.47 56.508 166.47 55.8333Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 164.016 61.7064C 164.016 61.0316 163.469 60.4847 162.794 60.4847C 162.12 60.4847 161.573 61.0316 161.573 61.7064C 161.573 62.3811 162.12 62.9281 162.794 62.9281C 163.469 62.9281 164.016 62.3811 164.016 61.7064Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 155.746 59.8381C 155.746 59.1634 155.199 58.6164 154.525 58.6164C 153.85 58.6164 153.303 59.1634 153.303 59.8381C 153.303 60.5129 153.85 61.0598 154.525 61.0598C 155.199 61.0598 155.746 60.5129 155.746 59.8381Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 159.554 42.2409C 159.554 41.5662 159.007 41.0192 158.332 41.0192C 157.657 41.0192 157.11 41.5662 157.11 42.2409C 157.11 42.9156 157.657 43.4626 158.332 43.4626C 159.007 43.4626 159.554 42.9156 159.554 42.2409Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 168.267 40.0173C 168.267 39.3426 167.72 38.7956 167.045 38.7956C 166.37 38.7956 165.823 39.3426 165.823 40.0173C 165.823 40.6921 166.37 41.239 167.045 41.239C 167.72 41.239 168.267 40.6921 168.267 40.0173Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 173.894 44.1184C 173.894 43.4437 173.347 42.8967 172.673 42.8967C 171.998 42.8967 171.451 43.4437 171.451 44.1184C 171.451 44.7931 171.998 45.3401 172.673 45.3401C 173.347 45.3401 173.894 44.7931 173.894 44.1184Z' fill='#00c0c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 158.471 54.4061C 158.471 53.7314 157.924 53.1844 157.249 53.1844C 156.575 53.1844 156.028 53.7314 156.028 54.4061C 156.028 55.0809 156.575 55.6279 157.249 55.6279C 157.924 55.6279 158.471 55.0809 158.471 54.4061Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 176.978 62.3579C 176.978 61.6831 176.431 61.1362 175.756 61.1362C 175.081 61.1362 174.534 61.6831 174.534 62.3579C 174.534 63.0326 175.081 63.5796 175.756 63.5796C 176.431 63.5796 176.978 63.0326 176.978 62.3579Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 182.26 47.0758C 182.26 46.4011 181.713 45.8541 181.038 45.8541C 180.363 45.8541 179.816 46.4011 179.816 47.0758C 179.816 47.7506 180.363 48.2975 181.038 48.2975C 181.713 48.2975 182.26 47.7506 182.26 47.0758Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 174.875 67.0564C 174.875 66.3816 174.328 65.8346 173.653 65.8346C 172.979 65.8346 172.432 66.3816 172.432 67.0564C 172.432 67.7311 172.979 68.2781 173.653 68.2781C 174.328 68.2781 174.875 67.7311 174.875 67.0564Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 164.589 71.5221C 164.589 70.8474 164.042 70.3004 163.367 70.3004C 162.693 70.3004 162.146 70.8474 162.146 71.5221C 162.146 72.1969 162.693 72.7438 163.367 72.7438C 164.042 72.7438 164.589 72.1969 164.589 71.5221Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 183.774 51.3947C 183.774 50.72 183.227 50.173 182.552 50.173C 181.877 50.173 181.33 50.72 181.33 51.3947C 181.33 52.0694 181.877 52.6164 182.552 52.6164C 183.227 52.6164 183.774 52.0694 183.774 51.3947Z' fill='#c000c0'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 179.704 51.1953C 179.704 50.5205 179.157 49.9735 178.483 49.9735C 177.808 49.9735 177.261 50.5205 177.261 51.1953C 177.261 51.87 177.808 52.417 178.483 52.417C 179.157 52.417 179.704 51.87 179.704 51.1953Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 155.54 64.5419C 155.54 63.8671 154.993 63.3201 154.318 63.3201C 153.644 63.3201 153.097 63.8671 153.097 64.5419C 153.097 65.2166 153.644 65.7636 154.318 65.7636C 154.993 65.7636 155.54 65.2166 155.54 64.5419Z' fill='#00c000'/>\n\x3C/g>\n\x3Cg transform='translate(102.802 111.311)scale(.996264)'>\n\x3Cpath d='M 148.034 49.9319C 135.048 45.3508 121.426 42.8074 107.652 42.3967C 99.4776 4",type:"svgGraphic"},uuid:"0|8"},$R[293]={content:$R[294]={type:"text",text:"Once a sample is selected, the poll is conducted through carefully designed surveys. The questions must be neutral and clear. A poorly worded question can easily influence the answer, leading to inaccurate results."},uuid:"0|9"},$R[295]={content:$R[296]={type:"table",markdown:"| Good Question | Bad Question |\n|---|---|\n| Which candidate do you plan to vote for? | Do you support the highly respected Candidate A, or the controversial Candidate B? |\n| What is your opinion on the new tax proposal? | Do you agree with the unfair tax proposal that will hurt working families? |"},uuid:"0|10"},$R[297]={content:$R[298]={type:"header",text:"Understanding the Numbers"},uuid:"0|11"},$R[299]={content:$R[300]={type:"text",text:"When you see poll results, they're usually presented as percentages. For example, a poll might report that Candidate A has 48% support and Candidate B has 45% support. But there's always a degree of uncertainty, because the poll is based on a sample, not the entire population."},uuid:"0|12"},$R[301]={content:$R[302]={type:"text",text:"This uncertainty is measured by the **margin of error**. It's usually expressed as a plus-or-minus percentage, like ±3%. This means the actual support for a candidate is likely within 3 percentage points of the reported number. In our example, Candidate A's true support could be as low as 45% or as high as 51%. Candidate B's support could be between 42% and 48%."},uuid:"0|13"},$R[303]={content:$R[304]={type:"blockquoteWithCitation",text:"Here’s the most important thing to remember about election polls: they are not instruments of prediction.",assetId:3245727},uuid:"0|14"},$R[305]={content:$R[306]={type:"text",text:"Because the ranges for both candidates overlap, the race is considered a statistical tie, or \"too close to call.\" The 3% difference is smaller than the poll's margin of error. It's a snapshot in time, not a crystal ball."},uuid:"0|15"},$R[307]={content:$R[308]={type:"quiz",questions:$R[309]=[$R[310]={text:"What is the primary goal of an election poll?",options:$R[311]=[$R[312]={text:"To provide a snapshot of public opinion at a specific moment in time.",followup:"Correct! Polls are designed to capture the public's views at the time the survey is conducted, like taking a photograph.",isRightAnswer:!0},$R[313]={text:"To influence undecided voters to support a particular candidate.",followup:"Incorrect. While poll results can influence people, the goal of a methodologically sound poll is to measure opinion, not to shape it.",isRightAnswer:!1},$R[314]={text:"To count every single voter's preference before an election.",followup:"Incorrect. Polls use a smaller sample because surveying the entire population (a census) is impractical.",isRightAnswer:!1},$R[315]={text:"To predict the exact final vote count of an election.",followup:"Incorrect. Polls provide an estimate, not a guaranteed prediction. The margin of error reflects this uncertainty.",isRightAnswer:!1}]},$R[316]={text:"Why do pollsters use sampling?",options:$R[317]=[$R[318]={text:"Legal regulations prohibit surveying an entire population for an election poll.",followup:"Incorrect. The limitation is practical and financial, not legal.",isRightAnswer:!1},$R[319]={text:"It allows pollsters to exclusively target voters who are undecided.",followup:"Incorrect. The goal is to create a *representative* sample, which must include voters of all preferences to accurately reflect the overall population.",isRightAnswer:!1},$R[320]={text:"Samples provide more accurate results than surveying the entire population.",followup:"Incorrect. Surveying the entire population (a census) would be more accurate, but it is not feasible for most polls.",isRightAnswer:!1},$R[321]={text:"It is generally impractical and too expensive to survey every individual in a large population.",followup:"That's right. Sampling allows pollsters to get a reasonably accurate picture of public opinion without the immense cost and time required to contact everyone.",isRightAnswer:!0}]},$R[322]={text:"For a poll's sample to be considered ________, its demographics should mirror the proportions of the larger population.",options:$R[323]=[$R[324]={text:"biased",followup:"Incorrect. A biased sample is one that does *not* accurately reflect the larger population.",isRightAnswer:!1},$R[325]={text:"representative",followup:"Correct! A representative sample is like a miniature version of the entire voting population, matching its key demographic characteristics.",isRightAnswer:!0},$R[326]={text:"random",followup:"Not quite. 'Random' describes how the sample is chosen (giving everyone an equal chance), but 'representative' describes how well it matches the population.",isRightAnswer:!1},$R[327]={text:"conclusive",followup:"Incorrect. No sample can be perfectly conclusive because of the inherent margin of error. 'Representative' is the term for a sample that demographically mirrors the population.",isRightAnswer:!1}]},$R[328]={text:"A poll reports Candidate A with 52% support and Candidate B with 48% support, with a margin of error of ±3%. What is the most accurate conclusion?",options:$R[329]=[$R[330]={text:"Candidate A has a clear and insurmountable lead over Candidate B.",followup:"Incorrect. The 4-point lead is within the poll's ±3% margin of error, meaning it is not a statistically significant lead.",isRightAnswer:!1},$R[331]={text:"The race is a statistical tie because the candidates' support ranges overlap.",followup:"Correct! Candidate A's true support could be as low as 49% (52-3), and Candidate B's could be as high as 51% (48+3). Since these ranges overlap, the lead is within the margin of error.",isRightAnswer:!0},$R[332]={text:"The poll is inaccurate because the percentages do not add up to 100%.",followup:"Incorrect. The percentages often don't add to 100% because of rounding or undecided/third-party voters.",isRightAnswer:!1},$R[333]={text:"Candidate A is guaranteed to win the election.",followup:"Incorrect. A poll is not a prediction. Also, the margin of error shows the race is very close.",isRightAnswer:!1}]},$R[334]={text:"The wording of a poll question must be neutral and clear to avoid influencing the results.",options:$R[335]=[$R[336]={text:"True",followup:"Correct. Leading or confusing questions can introduce bias and produce inaccurate results that don't reflect true public opinion.",isRightAnswer:!0},$R[337]={text:"False",followup:"Incorrect. The way a question is phrased can significantly affect how people respond, so neutrality is crucial for accuracy.",isRightAnswer:!1}]}]},uuid:"0|16"}]},$R[338]={uuid:"1",title:"Understanding Sampling and Bias",includesKnowledgeBase:!1,hasDemonstratedMastery:!1,streaming:!1,blocks:$R[339]=[$R[340]={content:$R[341]={type:"header",text:"The Perfect Sample"},uuid:"1|0"},$R[342]={content:$R[343]={type:"text",text:"Think of a giant pot of soup. To know if it's seasoned correctly, you don't need to eat the whole thing. You just need one spoonful. But for that spoonful to tell you the truth, it must have a little bit of everything in it—the broth, the vegetables, the meat. If you only scoop from the top layer of broth, you'll get a misleading taste.\n\nAn election poll works the same way. The goal is to get a small sample that perfectly mirrors the entire population of voters. This is called a **representative sample**. It should have the same proportions of age groups, genders, ethnicities, and geographic locations as the country itself. Getting this spoonful just right is the hardest part of polling."},uuid:"1|1"},$R[344]={content:$R[345]={type:"text",text:"While simple random sampling, where everyone has an equal chance of being picked, is the theoretical ideal, it's often impractical. Pollsters can't just pull names from a hat containing every voter. Instead, they often use a more sophisticated method called **stratified sampling**.\n\nWith this technique, pollsters first divide the population into different subgroups, or 'strata.' These groups might be based on state, age, race, or gender. Then, they draw random samples from within each of these groups, making sure the size of each sample is proportional to the group's size in the overall population. This ensures that no single demographic is accidentally overrepresented or ignored."},uuid:"1|2"},$R[346]={content:$R[347]={svgMarkup:"\x3C?xml version='1.0' encoding='UTF-8'?>\n\x3C!-- This file was generated by dvisvgm 2.11.1 -->\n\x3Csvg version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' width='340.15748pt' height='217.249292pt' viewBox='107.395916 169.632759 340.15748 217.249292'>\n\x3Cdefs>\n\x3Cpath id='g0-40' d='M3.297634 2.391034C3.297634 2.361146 3.297634 2.34122 3.128269 2.171856C1.882939 .916563 1.564134-.966376 1.564134-2.49066C1.564134-4.224159 1.942715-5.957659 3.16812-7.202989C3.297634-7.32254 3.297634-7.342466 3.297634-7.372354C3.297634-7.442092 3.257783-7.47198 3.198007-7.47198C3.098381-7.47198 2.201743-6.794521 1.613948-5.529265C1.105853-4.433375 .986301-3.327522 .986301-2.49066C.986301-1.713574 1.09589-.508095 1.643836 .617684C2.241594 1.843088 3.098381 2.49066 3.198007 2.49066C3.257783 2.49066 3.297634 2.460772 3.297634 2.391034Z'/>\n\x3Cpath id='g0-41' d='M2.879203-2.49066C2.879203-3.267746 2.769614-4.473225 2.221669-5.599004C1.62391-6.824408 .767123-7.47198 .667497-7.47198C.607721-7.47198 .56787-7.43213 .56787-7.372354C.56787-7.342466 .56787-7.32254 .757161-7.143213C1.733499-6.156912 2.30137-4.572852 2.30137-2.49066C2.30137-.787049 1.932752 .966376 .697385 2.221669C.56787 2.34122 .56787 2.361146 .56787 2.391034C.56787 2.450809 .607721 2.49066 .667497 2.49066C.767123 2.49066 1.663761 1.8132 2.251557 .547945C2.759651-.547945 2.879203-1.653798 2.879203-2.49066Z'/>\n\x3Cpath id='g0-65' d='M3.965131-6.933998C3.915318-7.063512 3.895392-7.13325 3.73599-7.13325S3.5467-7.073474 3.496887-6.933998L1.43462-.976339C1.255293-.468244 .856787-.318804 .318804-.308842V0C.547945-.009963 .976339-.029888 1.334994-.029888C1.643836-.029888 2.161893-.009963 2.480697 0V-.308842C1.982565-.308842 1.733499-.557908 1.733499-.816936C1.733499-.846824 1.743462-.946451 1.753425-.966376L2.211706-2.271482H4.672478L5.200498-.747198C5.210461-.707347 5.230386-.647572 5.230386-.607721C5.230386-.308842 4.672478-.308842 4.403487-.308842V0C4.762142-.029888 5.459527-.029888 5.838107-.029888C6.266501-.029888 6.724782-.019925 7.143213 0V-.308842H6.963885C6.366127-.308842 6.22665-.37858 6.117061-.707347L3.965131-6.933998ZM3.437111-5.818182L4.562889-2.580324H2.321295L3.437111-5.818182Z'/>\n\x3Cpath id='g0-66' d='M2.211706-3.646326V-6.097136C2.211706-6.425903 2.231631-6.495641 2.699875-6.495641H3.935243C4.901619-6.495641 5.250311-5.648817 5.250311-5.120797C5.250311-4.483188 4.762142-3.646326 3.656289-3.646326H2.211706ZM4.562889-3.556663C5.529265-3.745953 6.216687-4.383562 6.216687-5.120797C6.216687-5.987547 5.300125-6.804483 4.004981-6.804483H.358655V-6.495641H.597758C1.364882-6.495641 1.384807-6.386052 1.384807-6.027397V-.777086C1.384807-.418431 1.364882-.308842 .597758-.308842H.358655V0H4.26401C5.589041 0 6.485679-.886675 6.485679-1.823163C6.485679-2.689913 5.668742-3.437111 4.562889-3.556663ZM3.945205-.308842H2.699875C2.231631-.308842 2.211706-.37858 2.211706-.707347V-3.427148H4.084682C5.070984-3.427148 5.489415-2.500623 5.489415-1.833126C5.489415-1.125778 4.971357-.308842 3.945205-.308842Z'/>\n\x3Cpath id='g0-67' d='M.557908-3.407223C.557908-1.344956 2.171856 .219178 4.024907 .219178C5.648817 .219178 6.625156-1.165629 6.625156-2.321295C6.625156-2.420922 6.625156-2.49066 6.495641-2.49066C6.386052-2.49066 6.386052-2.430884 6.37609-2.331258C6.296389-.9066 5.230386-.089664 4.144458-.089664C3.536737-.089664 1.58406-.428394 1.58406-3.39726C1.58406-6.37609 3.526775-6.714819 4.134496-6.714819C5.220423-6.714819 6.107098-5.808219 6.306351-4.353674C6.326276-4.214197 6.326276-4.184309 6.465753-4.184309C6.625156-4.184309 6.625156-4.214197 6.625156-4.423412V-6.784558C6.625156-6.953923 6.625156-7.023661 6.515567-7.023661C6.475716-7.023661 6.435866-7.023661 6.356164-6.90411L5.858032-6.166874C5.489415-6.525529 4.98132-7.023661 4.024907-7.023661C2.161893-7.023661 .557908-5.439601 .557908-3.407223Z'/>\n\x3Cpath id='g0-72' d='M6.107098-6.027397C6.107098-6.386052 6.127024-6.495641 6.894147-6.495641H7.13325V-6.804483C6.784558-6.774595 6.047323-6.774595 5.668742-6.774595S4.542964-6.774595 4.194271-6.804483V-6.495641H4.433375C5.200498-6.495641 5.220423-6.386052 5.220423-6.027397V-3.696139H2.241594V-6.027397C2.241594-6.386052 2.261519-6.495641 3.028643-6.495641H3.267746V-6.804483C2.919054-6.774595 2.181818-6.774595 1.803238-6.774595S.67746-6.774595 .328767-6.804483V-6.495641H.56787C1.334994-6.495641 1.354919-6.386052 1.354919-6.027397V-.777086C1.354919-.418431 1.334994-.308842 .56787-.308842H.328767V0C.67746-.029888 1.414695-.029888 1.793275-.029888S2.919054-.029888 3.267746 0V-.308842H3.028643C2.261519-.308842 2.241594-.418431 2.241594-.777086V-3.387298H5.220423V-.777086C5.220423-.418431 5.200498-.308842 4.433375-.308842H4.194271V0C4.542964-.029888 5.280199-.029888 5.65878-.029888S6.784558-.029888 7.13325 0V-.308842H6.894147C6.127024-.308842 6.107098-.418431 6.107098-.777086V-6.027397Z'/>\n\x3Cpath id='g0-80' d='M2.261519-3.148194H3.945205C5.140722-3.148194 6.216687-3.955168 6.216687-4.951432C6.216687-5.927771 5.230386-6.804483 3.865504-6.804483H.348692V-6.495641H.587796C1.354919-6.495641 1.374844-6.386052 1.374844-6.027397V-.777086C1.374844-.418431 1.354919-.308842 .587796-.308842H.348692V0C.697385-.029888 1.43462-.029888 1.8132-.029888S2.938979-.029888 3.287671 0V-.308842H3.048568C2.281445-.308842 2.261519-.418431 2.261519-.777086V-3.148194ZM2.231631-3.407223V-6.097136C2.231631-6.425903 2.251557-6.495641 2.719801-6.495641H3.606476C5.190535-6.495641 5.190535-5.439601 5.190535-4.951432C5.190535-4.483188 5.190535-3.407223 3.606476-3.407223H2.231631Z'/>\n\x3Cpath id='g0-82' d='M2.231631-3.516812V-6.097136C2.231631-6.326276 2.231631-6.445828 2.450809-6.475716C2.550436-6.495641 2.839352-6.495641 3.038605-6.495641C3.935243-6.495641 5.051059-6.455791 5.051059-5.011208C5.051059-4.323786 4.811955-3.516812 3.337484-3.516812H2.231631ZM4.333748-3.387298C5.300125-3.626401 6.07721-4.234122 6.07721-5.011208C6.07721-5.967621 4.941469-6.804483 3.476961-6.804483H.348692V-6.495641H.587796C1.354919-6.495641 1.374844-6.386052 1.374844-6.027397V-.777086C1.374844-.418431 1.354919-.308842 .587796-.308842H.348692V0C.707347-.029888 1.414695-.029888 1.803238-.029888S2.899128-.029888 3.257783 0V-.308842H3.01868C2.251557-.308842 2.231631-.418431 2.231631-.777086V-3.297634H3.377335C3.536737-3.297634 3.955168-3.297634 4.303861-2.958904C4.682441-2.600249 4.682441-2.291407 4.682441-1.62391C4.682441-.976339 4.682441-.577833 5.090909-.199253C5.499377 .159402 6.047323 .219178 6.346202 .219178C7.123288 .219178 7.292653-.597758 7.292653-.876712C7.292653-.936488 7.292653-1.046077 7.163138-1.046077C7.053549-1.046077 7.053549-.956413 7.043587-.886675C6.983811-.179328 6.635118 0 6.386052 0C5.897883 0 5.818182-.508095 5.678705-1.43462L5.549191-2.231631C5.369863-2.86924 4.881694-3.198007 4.333748-3.387298Z'/>\n\x3Cpath id='g0-83' d='M3.476961-3.865504L2.201743-4.174346C1.58406-4.323786 1.195517-4.861768 1.195517-5.439601C1.195517-6.136986 1.733499-6.744707 2.510585-6.744707C4.174346-6.744707 4.393524-5.110834 4.4533-4.662516C4.463263-4.60274 4.463263-4.542964 4.572852-4.542964C4.702366-4.542964 4.702366-4.592777 4.702366-4.782067V-6.784558C4.702366-6.953923 4.702366-7.023661 4.592777-7.023661C4.523039-7.023661 4.513076-7.013699 4.443337-6.894147L4.094645-6.326276C3.795766-6.615193 3.387298-7.023661 2.500623-7.023661C1.39477-7.023661 .557908-6.146949 .557908-5.090909C.557908-4.26401 1.085928-3.536737 1.863014-3.267746C1.972603-3.227895 2.480697-3.108344 3.178082-2.938979C3.447073-2.86924 3.745953-2.799502 4.024907-2.430884C4.234122-2.171856 4.333748-1.843088 4.333748-1.514321C4.333748-.806974 3.835616-.089664 2.998755-.089664C2.709838-.089664 1.952677-.139477 1.424658-.627646C.846824-1.165629 .816936-1.803238 .806974-2.161893C.797011-2.261519 .71731-2.261519 .687422-2.261519C.557908-2.261519 .557908-2.191781 .557908-2.012453V-.019925C.557908 .14944 .557908 .219178 .667497 .219178C.737235 .219178 .747198 .199253 .816936 .089664C.816936 .079701 .846824 .049813 1.175592-.478207C1.484433-.139477 2.122042 .219178 3.008717 .219178C4.174346 .219178 4.971357-.757161 4.971357-1.853051C4.971357-2.849315 4.313823-3.666252 3.476961-3.865504Z'/>\n\x3Cpath id='g0-97' d='M3.317559-.757161C3.35741-.358655 3.626401 .059776 4.094645 .059776C4.303861 .059776 4.911582-.079701 4.911582-.886675V-1.444583H4.662516V-.886675C4.662516-.308842 4.41345-.249066 4.303861-.249066C3.975093-.249066 3.935243-.697385 3.935243-.747198V-2.739726C3.935243-3.158157 3.935243-3.5467 3.576588-3.915318C3.188045-4.303861 2.689913-4.463263 2.211706-4.463263C1.39477-4.463263 .707347-3.995019 .707347-3.337484C.707347-3.038605 .9066-2.86924 1.165629-2.86924C1.444583-2.86924 1.62391-3.068493 1.62391-3.327522C1.62391-3.447073 1.574097-3.775841 1.115816-3.785803C1.384807-4.134496 1.872976-4.244085 2.191781-4.244085C2.67995-4.244085 3.247821-3.855542 3.247821-2.968867V-2.600249C2.739726-2.570361 2.042341-2.540473 1.414695-2.241594C.667497-1.902864 .418431-1.384807 .418431-.946451C.418431-.139477 1.384807 .109589 2.012453 .109589C2.669988 .109589 3.128269-.288917 3.317559-.757161ZM3.247821-2.391034V-1.39477C3.247821-.448319 2.530511-.109589 2.082192-.109589C1.594022-.109589 1.185554-.458281 1.185554-.956413C1.185554-1.504359 1.603985-2.331258 3.247821-2.391034Z'/>\n\x3Cpath id='g0-101' d='M1.115816-2.510585C1.175592-3.995019 2.012453-4.244085 2.351183-4.244085C3.377335-4.244085 3.476961-2.899128 3.476961-2.510585H1.115816ZM1.105853-2.30137H3.88543C4.104608-2.30137 4.134496-2.30137 4.134496-2.510585C4.134496-3.496887 3.596513-4.463263 2.351183-4.463263C1.195517-4.463263 .278954-3.437111 .278954-2.191781C.278954-.856787 1.325031 .109589 2.470735 .109589C3.686177 .109589 4.134496-.996264 4.134496-1.185554C4.134496-1.285181 4.054795-1.305106 4.004981-1.305106C3.915318-1.305106 3.895392-1.24533 3.875467-1.165629C3.526775-.139477 2.630137-.139477 2.530511-.139477C2.032379-.139477 1.633873-.438356 1.404732-.806974C1.105853-1.285181 1.105853-1.942715 1.105853-2.30137Z'/>\n\x3Cpath id='g0-103' d='M2.211706-1.713574C1.344956-1.713574 1.344956-2.709838 1.344956-2.938979C1.344956-3.20797 1.354919-3.526775 1.504359-3.775841C1.58406-3.895392 1.8132-4.174346 2.211706-4.174346C3.078456-4.174346 3.078456-3.178082 3.078456-2.948941C3.078456-2.67995 3.068493-2.361146 2.919054-2.11208C2.839352-1.992528 2.610212-1.713574 2.211706-1.713574ZM1.05604-1.325031C1.05604-1.364882 1.05604-1.594022 1.225405-1.793275C1.613948-1.514321 2.022416-1.484433 2.211706-1.484433C3.138232-1.484433 3.825654-2.171856 3.825654-2.938979C3.825654-3.307597 3.666252-3.676214 3.417186-3.905355C3.775841-4.244085 4.134496-4.293898 4.313823-4.293898C4.333748-4.293898 4.383562-4.293898 4.41345-4.283935C4.303861-4.244085 4.254047-4.134496 4.254047-4.014944C4.254047-3.845579 4.383562-3.726027 4.542964-3.726027C4.64259-3.726027 4.83188-3.795766 4.83188-4.024907C4.83188-4.194271 4.712329-4.513076 4.323786-4.513076C4.124533-4.513076 3.686177-4.4533 3.267746-4.044832C2.849315-4.373599 2.430884-4.403487 2.211706-4.403487C1.285181-4.403487 .597758-3.716065 .597758-2.948941C.597758-2.510585 .816936-2.132005 1.066002-1.92279C.936488-1.77335 .757161-1.444583 .757161-1.09589C.757161-.787049 .886675-.408468 1.195517-.209215C.597758-.039851 .278954 .388543 .278954 .787049C.278954 1.504359 1.265255 2.052304 2.480697 2.052304C3.656289 2.052304 4.692403 1.544209 4.692403 .767123C4.692403 .418431 4.552927-.089664 4.044832-.368618C3.516812-.647572 2.938979-.647572 2.331258-.647572C2.082192-.647572 1.653798-.647572 1.58406-.657534C1.265255-.697385 1.05604-1.006227 1.05604-1.325031ZM2.49066 1.823163C1.484433 1.823163 .797011 1.315068 .797011 .787049C.797011 .328767 1.175592-.039851 1.613948-.069738H2.201743C3.058531-.069738 4.174346-.069738 4.174346 .787049C4.174346 1.325031 3.466999 1.823163 2.49066 1.823163Z'/>\n\x3Cpath id='g0-105' d='M1.763387-4.403487L.368618-4.293898V-3.985056C1.016189-3.985056 1.105853-3.92528 1.105853-3.437111V-.757161C1.105853-.308842 .996264-.308842 .328767-.308842V0C.647572-.009963 1.185554-.029888 1.424658-.029888C1.77335-.029888 2.122042-.009963 2.460772 0V-.308842C1.803238-.308842 1.763387-.358655 1.763387-.747198V-4.403487ZM1.803238-6.136986C1.803238-6.455791 1.554172-6.665006 1.275218-6.665006C.966376-6.665006 .747198-6.396015 .747198-6.136986C.747198-5.867995 .966376-5.608966 1.275218-5.608966C1.554172-5.608966 1.803238-5.818182 1.803238-6.136986Z'/>\n\x3Cpath id='g0-108' d='M1.763387-6.914072L.328767-6.804483V-6.495641C1.026152-6.495641 1.105853-6.425903 1.105853-5.937733V-.757161C1.105853-.308842 .996264-.308842 .328767-.308842V0C.657534-.009963 1.185554-.029888 1.43462-.029888S2.171856-.009963 2.540473 0V-.308842C1.872976-.308842 1.763387-.308842 1.763387-.757161V-6.914072Z'/>\n\x3Cpath id='g0-109' d='M1.09589-3.427148V-.757161C1.09589-.308842 .986301-.308842 .318804-.308842V0C.667497-.009963 1.175592-.029888 1.444583-.029888C1.703611-.029888 2.221669-.009963 2.560399 0V-.308842C1.892902-.308842 1.783313-.308842 1.783313-.757161V-2.590286C1.783313-3.626401 2.49066-4.184309 3.128269-4.184309C3.755915-4.184309 3.865504-3.646326 3.865504-3.078456V-.757161C3.865504-.308842 3.755915-.308842 3.088418-.308842V0C3.437111-.009963 3.945205-.029888 4.214197-.029888C4.473225-.029888 4.991283-.009963 5.330012 0V-.308842C4.662516-.308842 4.552927-.308842 4.552927-.757161V-2.590286C4.552927-3.626401 5.260274-4.184309 5.897883-4.184309C6.525529-4.184309 6.635118-3.646326 6.635118-3.078456V-.757161C6.635118-.308842 6.525529-.308842 5.858032-.308842V0C6.206725-.009963 6.714819-.029888 6.983811-.029888C7.242839-.029888 7.760897-.009963 8.099626 0V-.308842C7.581569-.308842 7.332503-.308842 7.32254-.607721V-2.510585C7.32254-3.367372 7.32254-3.676214 7.013699-4.034869C6.874222-4.204234 6.545455-4.403487 5.967621-4.403487C5.13076-4.403487 4.692403-3.805729 4.523039-3.427148C4.383562-4.293898 3.646326-4.403487 3.198007-4.403487C2.470735-4.403487 2.002491-3.975093 1.723537-3.35741V-4.403487L.318804-4.293898V-3.985056C1.016189-3.985056 1.09589-3.915318 1.09589-3.427148Z'/>\n\x3Cpath id='g0-110' d='M1.09589-3.427148V-.757161C1.09589-.308842 .986301-.308842 .318804-.308842V0C.667497-.009963 1.175592-.029888 1.444583-.029888C1.703611-.029888 2.221669-.009963 2.560399 0V-.308842C1.892902-.308842 1.783313-.308842 1.783313-.757161V-2.590286C1.783313-3.626401 2.49066-4.184309 3.128269-4.184309C3.755915-4.184309 3.865504-3.646326 3.865504-3.078456V-.757161C3.865504-.308842 3.755915-.308842 3.088418-.308842V0C3.437111-.009963 3.945205-.029888 4.214197-.029888C4.473225-.029888 4.991283-.009963 5.330012 0V-.308842C4.811955-.308842 4.562889-.308842 4.552927-.607721V-2.510585C4.552927-3.367372 4.552927-3.676214 4.244085-4.034869C4.104608-4.204234 3.775841-4.403487 3.198007-4.403487C2.470735-4.403487 2.002491-3.975093 1.723537-3.35741V-4.403487L.318804-4.293898V-3.985056C1.016189-3.985056 1.09589-3.915318 1.09589-3.427148Z'/>\n\x3Cpath id='g0-111' d='M4.692403-2.132005C4.692403-3.407223 3.696139-4.463263 2.49066-4.463263C1.24533-4.463263 .278954-3.377335 .278954-2.132005C.278954-.846824 1.315068 .109589 2.480697 .109589C3.686177 .109589 4.692403-.86675 4.692403-2.132005ZM2.49066-.139477C2.062267-.139477 1.62391-.348692 1.354919-.806974C1.105853-1.24533 1.105853-1.853051 1.105853-2.211706C1.105853-2.600249 1.105853-3.138232 1.344956-3.576588C1.613948-4.034869 2.082192-4.244085 2.480697-4.244085C2.919054-4.244085 3.347447-4.024907 3.606476-3.596513S3.865504-2.590286 3.865504-2.211706C3.865504-1.853051 3.865504-1.315068 3.646326-.876712C3.427148-.428394 2.988792-.139477 2.49066-.139477Z'/>\n\x3Cpath id='g0-112' d='M1.713574-3.745953V-4.403487L.278954-4.293898V-3.985056C.986301-3.985056 1.05604-3.92528 1.05604-3.486924V1.175592C1.05604 1.62391 .946451 1.62391 .278954 1.62391V1.932752C.617684 1.92279 1.135741 1.902864 1.39477 1.902864C1.663761 1.902864 2.171856 1.92279 2.520548 1.932752V1.62391C1.853051 1.62391 1.743462 1.62391 1.743462 1.175592V-.498132V-.587796C1.793275-.428394 2.211706 .109589 2.968867 .109589C4.154421 .109589 5.190535-.86675 5.190535-2.15193C5.190535-3.417186 4.224159-4.403487 3.108344-4.403487C2.331258-4.403487 1.912827-3.965131 1.713574-3.745953ZM1.743462-1.135741V-3.35741C2.032379-3.865504 2.520548-4.154421 3.028643-4.154421C3.755915-4.154421 4.363636-3.277709 4.363636-2.15193C4.363636-.946451 3.666252-.109589 2.929016-.109589C2.530511-.109589 2.15193-.308842 1.882939-.71731C1.743462-.926526 1.743462-.936488 1.743462-1.135741Z'/>\n\x3Cpath id='g0-114' d='M1.663761-3.307597V-4.403487L.278954-4.293898V-3.985056C.976339-3.985056 1.05604-3.915318 1.05604-3.427148V-.757161C1.05604-.308842 .946451-.308842 .278954-.308842V0C.667497-.009963 1.135741-.029888 1.414695-.029888C1.8132-.029888 2.281445-.029888 2.67995 0V-.308842H2.470735C1.733499-.308842 1.713574-.418431 1.713574-.777086V-2.311333C1.713574-3.297634 2.132005-4.184309 2.889166-4.184309C2.958904-4.184309 2.978829-4.184309 2.998755-4.174346C2.968867-4.164384 2.769614-4.044832 2.769614-3.785803C2.769614-3.506849 2.978829-3.35741 3.198007-3.35741C3.377335-3.35741 3.626401-3.476961 3.626401-3.795766S3.317559-4.403487 2.889166-4.403487C2.161893-4.403487 1.803238-3.73599 1.663761-3.307597Z'/>\n\x3Cpath id='g0-115' d='M2.072229-1.932752C2.291407-1.892902 3.108344-1.733499 3.108344-1.016189C3.108344-.508095 2.759651-.109589 1.982565-.109589C1.145704-.109589 .787049-.67746 .597758-1.524284C.56787-1.653798 .557908-1.693649 .458281-1.693649C.328767-1.693649 .328767-1.62391 .328767-1.444583V-.129514C.328767 .039851 .328767 .109589 .438356 .109589C.488169 .109589 .498132 .099626 .687422-.089664C.707347-.109589 .707347-.129514 .886675-.318804C1.325031 .099626 1.77335 .109589 1.982565 .109589C3.128269 .109589 3.58655-.557908 3.58655-1.275218C3.58655-1.803238 3.287671-2.102117 3.16812-2.221669C2.839352-2.540473 2.450809-2.620174 2.032379-2.699875C1.474471-2.809465 .806974-2.938979 .806974-3.516812C.806974-3.865504 1.066002-4.273973 1.92279-4.273973C3.01868-4.273973 3.068493-3.377335 3.088418-3.068493C3.098381-2.978829 3.188045-2.978829 3.20797-2.978829C3.337484-2.978829 3.337484-3.028643 3.337484-3.217933V-4.224159C3.337484-4.393524 3.337484-4.463263 3.227895-4.463263C3.178082-4.463263 3.158157-4.463263 3.028643-4.343711C2.998755-4.303861 2.899128-4.214197 2.859278-4.184309C2.480697-4.463263 2.072229-4.463263 1.92279-4.463263C.707347-4.463263 .328767-3.795766 .328767-3.237858C.328767-2.889166 .488169-2.610212 .757161-2.391034C1.075965-2.132005 1.354919-2.072229 2.072229-1.932752Z'/>\n\x3Cpath id='g0-116' d='M1.723537-3.985056H3.148194V-4.293898H1.723537V-6.127024H1.474471C1.464508-5.310087 1.165629-4.244085 .18929-4.204234V-3.985056H1.036115V-1.235367C1.036115-.009963 1.96264 .109589 2.321295 .109589C3.028643 .109589 3.307597-.597758 3.307597-1.235367V-1.803238H3.058531V-1.255293C3.058531-.518057 2.759651-.139477 2.391034-.139477C1.723537-.139477 1.723537-1.046077 1.723537-1.215442V-3.985056Z'/>\n\x3Cpath id='g0-117' d='M3.895392-.787049V.109589L5.330012 0V-.308842C4.632628-.308842 4.552927-.37858 4.552927-.86675V-4.403487L3.088418-4.293898V-3.985056C3.785803-3.985056 3.865504-3.915318 3.865504-3.427148V-1.653798C3.865504-.787049 3.387298-.109589 2.660025-.109589C1.823163-.109589 1.783313-.577833 1.783313-1.09589V-4.403487L.318804-4.293898V-3.985056C1.09589-3.985056 1.09589-3.955168 1.09589-3.068493V-1.574097C1.09589-.797011 1.09589 .109589 2.610212 .109589C3.16812 .109589 3.606476-.169365 3.895392-.787049Z'/>\n\x3Cpath id='g0-118' d='M4.144458-3.317559C4.234122-3.5467 4.403487-3.975093 5.061021-3.985056V-4.293898C4.83188-4.273973 4.542964-4.26401 4.313823-4.26401C4.07472-4.26401 3.616438-4.283935 3.447073-4.293898V-3.985056C3.815691-3.975093 3.92528-3.745953 3.92528-3.556663C3.92528-3.466999 3.905355-3.427148 3.865504-3.317559L2.849315-.777086L1.733499-3.556663C1.673724-3.686177 1.673724-3.706102 1.673724-3.726027C1.673724-3.985056 2.062267-3.985056 2.241594-3.985056V-4.293898C1.942715-4.283935 1.384807-4.26401 1.155666-4.26401C.886675-4.26401 .488169-4.273973 .18929-4.293898V-3.985056C.816936-3.985056 .856787-3.92528 .986301-3.616438L2.420922-.079701C2.480697 .059776 2.500623 .109589 2.630137 .109589S2.799502 .019925 2.839352-.079701L4.144458-3.317559Z'/>\n\x3C/defs>\n\x3Cg id='page1'>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 101.855 116.209C 101.855 88.2201 79.1662 65.531 51.1777 65.531C 23.1892 65.531 0.5 88.2201 0.5 116.209C 0.5 144.197 23.1892 166.886 51.1777 166.886C 79.1662 166.886 101.855 144.197 101.855 116.209Z' fill='none' stroke='#000000' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg fill='#333'>\n\x3Cuse x='134.655686' y='215.948661' xlink:href='#g0-80'/>\n\x3Cuse x='141.159084' y='215.948661' xlink:href='#g0-111'/>\n\x3Cuse x='146.140423' y='215.948661' xlink:href='#g0-112'/>\n\x3Cuse x='151.67524' y='215.948661' xlink:href='#g0-117'/>\n\x3Cuse x='157.210057' y='215.948661' xlink:href='#g0-108'/>\n\x3Cuse x='159.977466' y='215.948661' xlink:href='#g0-97'/>\n\x3Cuse x='164.958805' y='215.948661' xlink:href='#g0-116'/>\n\x3Cuse x='168.833178' y='215.948661' xlink:href='#g0-105'/>\n\x3Cuse x='171.600587' y='215.948661' xlink:href='#g0-111'/>\n\x3Cuse x='176.581926' y='215.948661' xlink:href='#g0-110'/>\n\x3Cuse x='123.060283' y='227.903829' xlink:href='#g0-40'/>\n\x3Cuse x='126.934657' y='227.903829' xlink:href='#g0-72'/>\n\x3Cuse x='134.406656' y='227.903829' xlink:href='#g0-101'/>\n\x3Cuse x='138.834507' y='227.903829' xlink:href='#g0-116'/>\n\x3Cuse x='142.708881' y='227.903829' xlink:href='#g0-101'/>\n\x3Cuse x='147.136733' y='227.903829' xlink:href='#g0-114'/>\n\x3Cuse x='151.038783' y='227.903829' xlink:href='#g0-111'/>\n\x3Cuse x='156.020122' y='227.903829' xlink:href='#g0-103'/>\n\x3Cuse x='161.001461' y='227.903829' xlink:href='#g0-101'/>\n\x3Cuse x='165.429313' y='227.903829' xlink:href='#g0-110'/>\n\x3Cuse x='170.96413' y='227.903829' xlink:href='#g0-101'/>\n\x3Cuse x='175.391982' y='227.903829' xlink:href='#g0-111'/>\n\x3Cuse x='180.373321' y='227.903829' xlink:href='#g0-117'/>\n\x3Cuse x='185.908138' y='227.903829' xlink:href='#g0-115'/>\n\x3Cuse x='189.837856' y='227.903829' xlink:href='#g0-41'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='27.1419' cy='144.905' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='75.6582' cy='87.9892' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='85.1722' cy='93.3958' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='28.3978' cy='92.816' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='33.7836' cy='101.869' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='46.4373' cy='116.92' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='27.9916' cy='143.3' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='88.0827' cy='129.313' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='15.702' cy='104.414' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='59.7397' cy='112.529' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='73.1987' cy='86.1706' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='73.8985' cy='99.6907' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='64.7408' cy='107.094' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='21.5883' cy='105.807' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='78.3723' cy='89.8492' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='34.5553' cy='82.9463' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='63.675' cy='151.384' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='59.2123' cy='120.233' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='19.0801' cy='97.5105' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='74.5054' cy='113.366' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='16.3052' cy='107.124' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='19.6907' cy='114.002' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='35.6853' cy='128.57' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='28.6215' cy='96.0556' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='20.7846' cy='98.4857' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='74.997' cy='100.752' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='56.2094' cy='126.812' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='23.2715' cy='105.851' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='15.6224' cy='140.352' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='21.1562' cy='131.937' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='11.6655' cy='110.133' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='12.1331' cy='114.192' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='40.5994' cy='89.5049' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='27.9406' cy='119.559' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='53.4306' cy='142.131' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='41.8671' cy='138.121' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='29.6923' cy='146.062' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='60.1744' cy='154.072' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='60.6403' cy='149.682' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='76.3693' cy='126.808' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='43.4096' cy='92.023' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='20.423' cy='136.795' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='67.6453' cy='96.6342' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='22.7483' cy='134.94' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='27.5735' cy='136.475' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='44.152' cy='108.434' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='22.9778' cy='140.067' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='75.8581' cy='127.378' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='55.7953' cy='79.4258' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='73.4933' cy='94.2128' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 228.549 40.1921C 228.549 26.1979 217.205 14.8533 203.211 14.8533C 189.216 14.8533 177.872 26.1979 177.872 40.1921C 177.872 54.1864 189.216 65.531 203.211 65.531C 217.205 65.531 228.549 54.1864 228.549 40.1921Z' fill='none' stroke='#000000' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg fill='#3380e6'>\n\x3Cuse x='286.452795' y='177.676791' xlink:href='#g0-83'/>\n\x3Cuse x='291.987612' y='177.676791' xlink:href='#g0-116'/>\n\x3Cuse x='295.861986' y='177.676791' xlink:href='#g0-114'/>\n\x3Cuse x='299.764036' y='177.676791' xlink:href='#g0-97'/>\n\x3Cuse x='304.745375' y='177.676791' xlink:href='#g0-116'/>\n\x3Cuse x='308.619749' y='177.676791' xlink:href='#g0-117'/>\n\x3Cuse x='314.154566' y='177.676791' xlink:href='#g0-109'/>\n\x3Cuse x='325.777666' y='177.676791' xlink:href='#g0-65'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='205.04' cy='21.1666' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='208.254' cy='44.3684' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='214.426' cy='30.5387' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='215.705' cy='27.3431' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='188.753' cy='51.3737' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='204.736' cy='52.9515' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='223.966' cy='40.6777' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='193.91' cy='30.4621' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='205.051' cy='59.7547' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='216.206' cy='51.8841' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='200.722' cy='35.267' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='193.119' cy='34.3496' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 228.549 116.209C 228.549 102.214 217.205 90.8698 203.211 90.8698C 189.216 90.8698 177.872 102.214 177.872 116.209C 177.872 130.203 189.216 141.547 203.211 141.547C 217.205 141.547 228.549 130.203 228.549 116.209Z' fill='none' stroke='#000000' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg fill='#1acc4d'>\n\x3Cuse x='286.660345' y='253.409304' xlink:href='#g0-83'/>\n\x3Cuse x='292.195162' y='253.409304' xlink:href='#g0-116'/>\n\x3Cuse x='296.069535' y='253.409304' xlink:href='#g0-114'/>\n\x3Cuse x='299.971586' y='253.409304' xlink:href='#g0-97'/>\n\x3Cuse x='304.952925' y='253.409304' xlink:href='#g0-116'/>\n\x3Cuse x='308.827299' y='253.409304' xlink:href='#g0-117'/>\n\x3Cuse x='314.362116' y='253.409304' xlink:href='#g0-109'/>\n\x3Cuse x='325.985216' y='253.409304' xlink:href='#g0-66'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='208.359' cy='123.136' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='199.394' cy='101.689' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='185.645' cy='107.764' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='207.542' cy='120.488' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='218.945' cy='127.154' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='222.847' cy='112.892' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='220.026' cy='108.29' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='196.516' cy='123.343' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 228.549 192.225C 228.549 178.231 217.205 166.886 203.211 166.886C 189.216 166.886 177.872 178.231 177.872 192.225C 177.872 206.219 189.216 217.564 203.211 217.564C 217.205 217.564 228.549 206.219 228.549 192.225Z' fill='none' stroke='#000000' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg fill='#ff991a'>\n\x3Cuse x='286.591161' y='329.141802' xlink:href='#g0-83'/>\n\x3Cuse x='292.125978' y='329.141802' xlink:href='#g0-116'/>\n\x3Cuse x='296.000352' y='329.141802' xlink:href='#g0-114'/>\n\x3Cuse x='299.902403' y='329.141802' xlink:href='#g0-97'/>\n\x3Cuse x='304.883742' y='329.141802' xlink:href='#g0-116'/>\n\x3Cuse x='308.758115' y='329.141802' xlink:href='#g0-117'/>\n\x3Cuse x='314.292932' y='329.141802' xlink:href='#g0-109'/>\n\x3Cuse x='325.916033' y='329.141802' xlink:href='#g0-67'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='196.087' cy='179.789' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='199.571' cy='204.565' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='202.63' cy='204.742' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='198.18' cy='199.345' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 274.159 161.819L 334.973 161.819L 334.973 70.5987L 274.159 70.5987L 274.159 161.819Z' fill='none' stroke='#000000' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg fill='#333'>\n\x3Cuse x='378.75367' y='221.21883' xlink:href='#g0-82'/>\n\x3Cuse x='386.087295' y='221.21883' xlink:href='#g0-101'/>\n\x3Cuse x='390.515146' y='221.21883' xlink:href='#g0-112'/>\n\x3Cuse x='396.049963' y='221.21883' xlink:href='#g0-114'/>\n\x3Cuse x='399.952014' y='221.21883' xlink:href='#g0-101'/>\n\x3Cuse x='404.379865' y='221.21883' xlink:href='#g0-115'/>\n\x3Cuse x='408.309583' y='221.21883' xlink:href='#g0-101'/>\n\x3Cuse x='412.737435' y='221.21883' xlink:href='#g0-110'/>\n\x3Cuse x='417.995503' y='221.21883' xlink:href='#g0-116'/>\n\x3Cuse x='421.869877' y='221.21883' xlink:href='#g0-97'/>\n\x3Cuse x='426.851216' y='221.21883' xlink:href='#g0-116'/>\n\x3Cuse x='430.72559' y='221.21883' xlink:href='#g0-105'/>\n\x3Cuse x='433.492998' y='221.21883' xlink:href='#g0-118'/>\n\x3Cuse x='438.474337' y='221.21883' xlink:href='#g0-101'/>\n\x3Cuse x='395.053659' y='233.173998' xlink:href='#g0-83'/>\n\x3Cuse x='400.588476' y='233.173998' xlink:href='#g0-97'/>\n\x3Cuse x='405.569815' y='233.173998' xlink:href='#g0-109'/>\n\x3Cuse x='413.872041' y='233.173998' xlink:href='#g0-112'/>\n\x3Cuse x='419.406858' y='233.173998' xlink:href='#g0-108'/>\n\x3Cuse x='422.174266' y='233.173998' xlink:href='#g0-101'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='284.191' cy='123.473' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='297.678' cy='114.986' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='312.914' cy='100.023' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='304.652' cy='114.544' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='299.57' cy='94.9207' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='289.368' cy='127.178' fill='#3380e6' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='307.217' cy='146.769' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='291.59' cy='121.138' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='303.265' cy='136.809' fill='#19cc4c' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Ccircle cx='309.346' cy='80.3952' fill='#ff9919' r='2'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 173.747 41.5633L 101.855 103.539' fill='none' stroke='#808080' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 173.421 41.1846C 171.103 41.3674 169.123 40.3345 168.993 40.183C 168.846 40.0126 168.948 39.8252 169.081 39.7109C 169.289 39.5314 169.409 39.5934 169.547 39.6391C 170.386 40.0381 172.184 40.898 174.844 40.1556C 175.169 40.0733 175.224 40.0596 175.338 40.1921C 175.452 40.3247 175.431 40.3763 175.301 40.6858C 174.175 43.2081 174.761 45.1127 175.032 46.0014C 175.057 46.145 175.1 46.2723 174.892 46.4519C 174.759 46.5661 174.559 46.6399 174.412 46.4695C 174.281 46.318 173.552 44.2077 174.074 41.942L 173.421 41.1846Z' fill='#808080'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 173.238 116.209L 101.855 116.209' fill='none' stroke='#808080' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 173.238 115.709C 171.363 114.334 170.538 112.259 170.538 112.059C 170.538 111.834 170.738 111.759 170.913 111.759C 171.188 111.759 171.238 111.884 171.313 112.009C 171.688 112.859 172.488 114.684 174.988 115.859C 175.288 116.009 175.338 116.034 175.338 116.209C 175.338 116.384 175.288 116.409 174.988 116.559C 172.488 117.734 171.688 119.559 171.313 120.409C 171.238 120.534 171.188 120.659 170.913 120.659C 170.738 120.659 170.538 120.584 170.538 120.359C 170.538 120.159 171.363 118.084 173.238 116.709L 173.238 115.709Z' fill='#808080'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 173.747 190.854L 101.855 128.878' fill='none' stroke='#808080' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 174.074 190.475C 173.552 188.21 174.281 186.099 174.412 185.948C 174.559 185.777 174.759 185.851 174.892 185.965C 175.1 186.145 175.057 186.272 175.032 186.416C 174.761 187.305 174.175 189.209 175.301 191.732C 175.431 192.041 175.452 192.093 175.338 192.225C 175.224 192.358 175.169 192.344 174.844 192.262C 172.184 191.519 170.386 192.379 169.547 192.778C 169.409 192.824 169.289 192.886 169.081 192.706C 168.948 192.592 168.846 192.405 168.993 192.234C 169.123 192.083 171.103 191.05 173.421 191.233L 174.074 190.475Z' fill='#808080'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 270.265 89.2697L 228.549 40.1921' fill='none' stroke='#808080' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 270.646 88.9459C 270.48 86.6268 271.526 84.6543 271.679 84.5248C 271.85 84.379 272.037 84.4829 272.15 84.6162C 272.328 84.8257 272.266 84.9448 272.219 85.0829C 271.814 85.9191 270.942 87.7106 271.666 90.3765C 271.746 90.7022 271.759 90.7565 271.626 90.8698C 271.492 90.9832 271.441 90.9612 271.132 90.8298C 268.618 89.6859 266.709 90.2584 265.819 90.5231C 265.675 90.5469 265.547 90.5898 265.369 90.3803C 265.256 90.2469 265.183 90.046 265.355 89.9002C 265.507 89.7707 267.622 89.0554 269.884 89.5936L 270.646 88.9459Z' fill='#808080'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 269.526 116.209L 228.549 116.209' fill='none' stroke='#808080' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 269.526 115.709C 267.651 114.334 266.826 112.259 266.826 112.059C 266.826 111.834 267.026 111.759 267.201 111.759C 267.476 111.759 267.526 111.884 267.601 112.009C 267.976 112.859 268.776 114.684 271.276 115.859C 271.576 116.009 271.626 116.034 271.626 116.209C 271.626 116.384 271.576 116.409 271.276 116.559C 268.776 117.734 267.976 119.559 267.601 120.409C 267.526 120.534 267.476 120.659 267.201 120.659C 267.026 120.659 266.826 120.584 266.826 120.359C 266.826 120.159 267.651 118.084 269.526 116.709L 269.526 115.709Z' fill='#808080'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 270.265 143.148L 228.549 192.225' fill='none' stroke='#808080' stroke-linecap='round' stroke-linejoin='round' stroke-miterlimit='10.0375' stroke-width='1'/>\n\x3C/g>\n\x3Cg transform='translate(107.396 169.633)scale(.996264)'>\n\x3Cpath d='M 269.884 142.824C 267.622 143.362 265.507 142.647 265.355 142.517C 265.183 142.371 265.256 142.17 265.369 142.037C 265.547 141.827 265.675 141.87 265.819 141.894C 266.709 142.159 268.618 142.731 271.132 141.587C 271.441 141.456 271.492 141.434 271.626 141.547C 271.759 141.661 271.746 141.715 271.666 142.041C 270.942 144.707 271.814 146.498 272.219 147.334C 272.266 147.473 272.328 147.592 272.15 147.801C 272.037 147.934 271.85 148.038 271.679 147.893C 271.526 147.763 270.48 145.791 270.646 143.471L 269.884 142.824Z' fill='#808080'/>\n\x3C/g>\n\x3C/g>\n\x3C/svg>",type:"svgGraphic"},uuid:"1|3"},$R[348]={content:$R[349]={type:"header",text:"When Samples Go Wrong"},uuid:"1|4"},$R[350]={content:$R[351]={type:"text",text:"Even with careful methods, things can go awry. Polls can be inaccurate not because of bad luck, but because of systematic errors called biases. A bias isn't just random error; it's an error that consistently pushes the results in one direction."},uuid:"1|5"},$R[352]={content:$R[353]={type:"realImage",url:"https://oboe-storage.s3.amazonaws.com/dev/imagesReal/v1/course-5964/8ca43f2f-d3b8-4896-b759-8591e23a82f9.png",attributionUrl:"https://commons.wikimedia.org/wiki/File:Statistical_bias_and_statistical_noise_illustration_No_text_in_image.png",caption:"Bias represents a systematic error, while noise represents random error. Pollsters aim for low bias and low noise."},uuid:"1|6"},$R[354]={content:$R[355]={type:"text",text:"**Sampling bias** is one of the biggest culprits. This happens when the method of selecting the sample favors certain people over others. The most famous example is the 1936 *Literary Digest* poll. It surveyed millions of people from telephone directories and car registration lists and boldly predicted a landslide victory for Alf Landon over Franklin D. Roosevelt. \n\nRoosevelt won in a landslide. The poll failed because, during the Great Depression, only wealthier Americans could afford cars and phones. The sample was huge, but it wasn't representative. It systematically excluded poorer voters, who overwhelmingly supported Roosevelt.\n\nToday, a similar bias can occur if a poll only calls landlines, missing younger, more mobile voters. Or if an online poll only captures the opinions of the very online, who don't necessarily represent everyone."},uuid:"1|7"},$R[356]={content:$R[357]={type:"header",text:"The Problem of Who Answers"},uuid:"1|8"},$R[358]={content:$R[359]={type:"text",text:"Another major hurdle is **nonresponse bias**. This occurs when the people who choose to participate in a poll are fundamentally different from those who don't. Imagine a poll about satisfaction with local government. People who are angry or have a problem they want to complain about might be far more likely to answer the call than those who are generally content. The result? The poll would show much higher dissatisfaction than actually exists in the community.\n\nGetting people to respond to polls is harder than ever. Decades ago, response rates were high, but today, with caller ID and general skepticism, many people simply don't pick up or refuse to participate. This makes nonresponse bias a constant worry for pollsters. They have to ask: Are the people I managed to reach different in some important way from the ones I didn't?"},uuid:"1|9"},$R[360]={content:$R[361]={type:"blockquote",text:"Nonresponse bias isn't about who you try to contact, but about who actually answers."},uuid:"1|10"},$R[362]={content:$R[363]={type:"text",text:"To fight these biases, pollsters use a technique called **weighting**. After they collect their data, they compare their sample's demographics (age, gender, race, education, etc.) to known data about the entire population from sources like the Census Bureau. \n\nIf they find their sample has, for example, too few young men, they give the responses from the young men they *did* survey a little more weight. This adjustment helps make the final sample more closely mirror the population it's meant to represent. It's a crucial correction, but not a perfect cure. A poorly collected sample can't be completely fixed by weighting."},uuid:"1|11"},$R[364]={content:$R[365]={type:"blockquoteWithCitation",text:"Polls should state whether or not they are weighted, and good polls should provide details about the weighting.",assetId:3244306},uuid:"1|12"},$R[366]={content:$R[367]={type:"quiz",questions:$R[368]=[$R[369]={text:"What is the primary goal of creating a 'representative sample' in the context of polling?",options:$R[370]=[$R[371]={text:"To create a small group whose demographic characteristics accurately mirror those of the entire population.",followup:"Correct. Just like a single spoonful should taste like the whole pot of soup, a representative sample should reflect the entire population's makeup.",isRightAnswer:!0},$R[372]={text:"To survey the largest number of people possible to increase accuracy.",followup:"While a larger sample can reduce the margin of error, the primary goal is representation, not size. A huge but unrepresentative sample is not accurate.",isRightAnswer:!1},$R[373]={text:"To only poll people who have strong opinions on a subject.",followup:"This would lead to a biased sample, as it would overrepresent people with strong views and ignore those who are undecided or less engaged.",isRightAnswer:!1},$R[374]={text:"To ensure that every single person in the population has a chance to be polled.",followup:"This describes a census, not a sample. Sampling, by definition, involves studying a small part of a larger group.",isRightAnswer:!1}]},$R[375]={text:"The 1936 Literary Digest poll famously and incorrectly predicted a landslide victory for Alf Landon over Franklin D. Roosevelt. This failure is a classic example of what?",options:$R[376]=[$R[377]={text:"Nonresponse bias",followup:"While some nonresponse bias may have occurred, the primary error was the initial selection of the sample.",isRightAnswer:!1},$R[378]={text:"Stratified sampling",followup:"Stratified sampling is a valid method used to create a more representative sample; it is not a type of polling error.",isRightAnswer:!1},$R[379]={text:"Sampling bias",followup:"Correct. The poll sampled people from telephone directories and car registration lists. During the Great Depression, this systematically excluded poorer voters, who were not representative of the entire country.",isRightAnswer:!0},$R[380]={text:"Weighting",followup:"Weighting is a technique used to correct for biases after data is collected, not the source of the error itself.",isRightAnswer:!1}]},$R[381]={text:"A pollster divides the population into subgroups based on state and age, then draws a random, proportional sample from each of those subgroups. What is this technique called?",options:$R[382]=[$R[383]={text:"Stratified sampling",followup:"Correct. This method involves dividing the population into 'strata' (subgroups) and then sampling proportionally from each one.",isRightAnswer:!0},$R[384]={text:"Simple random sampling",followup:"Simple random sampling involves selecting from the entire population at once, without first dividing it into subgroups.",isRightAnswer:!1},$R[385]={text:"Systematic bias",followup:"This is a type of error, not a sampling methodology. Stratified sampling is actually a technique used to avoid bias.",isRightAnswer:!1},$R[386]={text:"Weighting",followup:"Weighting is a statistical adjustment made after the sample is collected, not the method of selecting the sample itself.",isRightAnswer:!1}]},$R[387]={text:"An online poll about a new city tax is shared widely on social media. The results show overwhelming opposition. However, people who feel strongly against the tax are far more likely to click the link and complete the survey than those who are content or indifferent. This is a clear example of:",options:$R[388]=[$R[389]={text:"Effective weighting",followup:"Weighting is a correction technique. The scenario describes a fundamental problem in who chose to participate.",isRightAnswer:!1},$R[390]={text:"A representative sample",followup:"This sample is not representative because it over-represents people with strong negative opinions.",isRightAnswer:!1},$R[391]={text:"Nonresponse bias",followup:"Correct. This occurs when the people who choose to respond to a poll are systematically different from those who do not, skewing the results.",isRightAnswer:!0}]},$R[392]={text:"A poorly collected, unrepresentative sample can always be fixed by using advanced weighting techniques.",options:$R[393]=[$R[394]={text:"True",followup:"This is false. While weighting is a crucial tool to adjust a sample and make it more representative, it cannot perfectly cure a sample that was fundamentally flawed from the beginning.",isRightAnswer:!1},$R[395]={text:"False",followup:"Correct. Weighting is an important correction tool, but it has limits. It cannot fully salvage a sample that was collected using a biased method or that completely misses certain demographics.",isRightAnswer:!0}]}]},uuid:"1|13"},$R[396]={content:$R[397]={type:"text",text:"Understanding these potential pitfalls is key to being a smart consumer of polling data. A poll's credibility depends heavily on how well it navigated the challenges of building a truly representative sample."},uuid:"1|14"}]},$R[398]={uuid:"2",title:"Statistical Significance and Margin of Error",includesKnowledgeBase:!1,hasDemonstratedMastery:!1,streaming:!1,blocks:$R[399]=[$R[400]={content:$R[401]={type:"header",text:"Reading the Fine Print"},uuid:"2|0"},$R[402]={content:$R[403]={type:"text",text:"Every poll you see comes with a bit of fine print: the margin of error. It's easy to overlook, but it's one of the most important parts of a poll. The margin of error isn't a measure of mistakes. Instead, it quantifies the uncertainty that comes from polling a sample of people instead of the entire population."},uuid:"2|1"},$R[404]={content:$R[405]={type:"text",text:"Think of it like tasting a spoonful of soup to judge the whole pot. The spoonful gives you a good idea of the overall flavor, but it might be slightly saltier or less spicy than the pot as a whole. The margin of error tells you how much that spoonful might differ from the full pot."},uuid:"2|2"},$R[406]={content:$R[407]={type:"blockquoteWithCitation",text:"The margin of sampling error (usually just “margin of error”) understates the amount of error in a poll because it reflects possible error from only one source: taking a sample of the population rather than interviewing everyone.",assetId:3245735},uuid:"2|3"},$R[408]={content:$R[409]={type:"header",text:"Plus or Minus What?"},uuid:"2|4"},$R[410]={content:$R[411]={type:"text",text:"You'll usually see the margin of error expressed as a \"plus or minus\" percentage, like ±3%. Let's say a poll shows Candidate A has 52% support with a margin of error of ±3%. This doesn't mean their support is exactly 52%. Instead, it means we can be reasonably sure their actual support among the whole population is somewhere between 49% (52% - 3%) and 55% (52% + 3%)."},uuid:"2|5"},$R[412]={content:$R[413]={type:"text",text:"This range is called the **confidence interval**. Pollsters typically use a 95% confidence level. This means that if they were to conduct the exact same poll 100 times, they would expect the results to fall within that margin of error 95 out of those 100 times."},uuid:"2|6"},$R[414]={content:$R[415]={type:"blockquoteWithCitation",text:"The margin of error of plus or minus 3 percentage points at the 95% confidence level means that if we fielded the same survey 100 times, we would expect the result to be within 3 percentage points of the true population value 95 of those times.",assetId:3245742},uuid:"2|7"},$R[416]={content:$R[417]={type:"header",text:"When a Race is Too Close to Call"},uuid:"2|8"},$R[418]={content:$R[419]={type:"text",text:"This is where the concept of **statistical significance** comes into play. It helps us determine if a poll's result is a meaningful finding or just random chance. When you hear that a political race is \"a statistical tie\" or \"too close to call,\" it means the candidates' levels of support are within the margin of error."},uuid:"2|9"},$R[420]={content:$R[421]={type:"text",text:"Imagine a poll shows Candidate A at 48% and Candidate B at 46%, with a margin of error of ±3%. Candidate A's true support is likely between 45% and 51%. Candidate B's true support is likely between 43% and 49%. Because these two ranges overlap, we can't be statistically confident that Candidate A is actually ahead. Their lead is not statistically significant."},uuid:"2|10"},$R[422]={content:$R[423]={type:"text",text:"For a candidate's lead to be statistically significant, their margin over the opponent must be greater than the poll's margin of error. This gives us confidence that the lead exists in the real world and isn't just a quirk of the specific sample of people who were polled."},uuid:"2|12"},$R[424]={content:$R[425]={type:"header",text:"Putting It All Together"},uuid:"2|13"},$R[426]={content:$R[427]={type:"text",text:"Understanding these concepts is key to being a savvy consumer of polls. When you see a poll, don't just look at the headline numbers. Look for the margin of error and the sample size. Ask yourself if the differences between candidates are larger than the margin of error."},uuid:"2|14"},$R[428]={content:$R[429]={type:"realImage",url:"https://oboe-storage.s3.amazonaws.com/dev/imagesReal/v1/42f819ce-daba-4a6b-b97d-cb67c9e7c1e5.png",attributionUrl:"https://commons.wikimedia.org/wiki/File:WQUADAS_Creation_and_Reliability_Survey.png",caption:"Assessing a poll's credibility involves checking multiple factors, including its methodology and statistical measures."},uuid:"2|15"},$R[430]={content:$R[431]={type:"text",text:"This quick check helps you distinguish between a meaningful lead and a race that's essentially tied. It allows you to see the uncertainty built into every poll and interpret the results with the nuance they deserve."},uuid:"2|16"},$R[432]={content:$R[433]={type:"quiz",questions:$R[434]=[$R[435]={text:"What does the margin of error in a poll primarily quantify?",options:$R[436]=[$R[437]={text:"The number of mistakes made by the pollsters during the survey.",followup:"This is incorrect. The margin of error isn't about mistakes; it's an inherent part of sampling.",isRightAnswer:!1},$R[438]={text:"The uncertainty from polling a sample instead of the entire population.",followup:"Correct! It measures the potential difference between the sample's opinion and the whole population's opinion.",isRightAnswer:!0},$R[439]={text:"The percentage of people who refused to participate in the poll.",followup:"This is known as the non-response rate, which is a separate metric from the margin of error.",isRightAnswer:!1},$R[440]={text:"The bias of the questions asked in the poll.",followup:"Question bias is a different type of polling error and is not measured by the margin of error.",isRightAnswer:!1}]},$R[441]={text:"A poll finds that 60% of voters support a new policy, with a margin of error of ±4%. What is the confidence interval for this poll?",options:$R[442]=[$R[443]={text:"58% to 62%",followup:"This seems to be using an incorrect margin of error.",isRightAnswer:!1},$R[444]={text:"60% to 64%",followup:"This only accounts for adding the margin of error, not subtracting it.",isRightAnswer:!1},$R[445]={text:"56% to 64%",followup:"That's right! You find the range by subtracting and adding the margin of error from the result (60% - 4% and 60% + 4%).",isRightAnswer:!0}]},$R[446]={text:"When a political race is described as a 'statistical tie,' it means the candidates' levels of support are within the poll's margin of error.",options:$R[447]=[$R[448]={text:"True",followup:"Correct. Because their support ranges overlap, we cannot be statistically confident that one candidate is actually ahead.",isRightAnswer:!0},$R[449]={text:"False",followup:"Incorrect. This is the precise definition of a statistical tie or a race that is 'too close to call.'",isRightAnswer:!1}]},$R[450]={text:"In a mayoral race, a poll shows Candidate Smith with 51% support and Candidate Jones with 49% support. The poll has a margin of error of ±3%. How should this result be interpreted?",options:$R[451]=[$R[452]={text:"The race is a 'statistical tie' because the candidates' support ranges overlap.",followup:"Exactly! Smith's support is between 48-54%, and Jones' is between 46-52%. Since these ranges overlap, we can't be sure who is ahead.",isRightAnswer:!0},$R[453]={text:"Candidate Smith has a statistically significant lead.",followup:"Incorrect. The lead (2%) is smaller than the margin of error (3%), so it is not statistically significant.",isRightAnswer:!1},$R[454]={text:"Candidate Jones is definitely losing the race.",followup:"Not necessarily. According to the confidence interval (46-52%), it's plausible that Jones's actual support is higher than Smith's.",isRightAnswer:!1},$R[455]={text:"The poll is inaccurate and should be disregarded.",followup:"The poll isn't necessarily inaccurate; it's just showing that the race is too close to call based on this sample.",isRightAnswer:!1}]},$R[456]={text:"For a candidate's lead to be considered 'statistically significant,' their lead over an opponent must be _______ the poll's margin of error.",options:$R[457]=[$R[458]={text:"equal to",followup:"Close, but for a lead to be confidently significant, it needs to be clearly outside the range of random sampling error.",isRightAnswer:!1},$R[459]={text:"greater than",followup:"Correct! A lead greater than the margin of error gives us confidence that it's a real lead and not just a quirk of the sample.",isRightAnswer:!0},$R[460]={text:"less than",followup:"Incorrect. If the lead is less than the margin of error, it could be due to random chance.",isRightAnswer:!1}]}]},uuid:"2|17"},$R[461]={content:$R[462]={type:"text",text:"By understanding margin of error and statistical significance, you can look past the headlines and get a more accurate picture of what a poll is really telling you."},uuid:"2|18"}]},$R[463]={uuid:"3",title:"Recognizing and Evaluating Polling Biases",includesKnowledgeBase:!1,hasDemonstratedMastery:!1,streaming:!1,blocks:$R[464]=[$R[465]={content:$R[466]={type:"header",text:"The Human Factor in Polls"},uuid:"3|0"},$R[467]={content:$R[468]={type:"text",text:"Even when a poll has a great sample and a low margin of error, the results can still be misleading. Why? Because polls involve asking questions of actual people, and people are complicated. Sometimes, what a person tells a pollster isn't what they truly believe or how they'll actually vote. This disconnect creates what are known as response biases."},uuid:"3|1"},$R[469]={content:$R[470]={type:"text",text:"Unlike sampling or nonresponse bias, which are about *who* is in the poll, response biases are about *what* those people say. They are subtle psychological forces that can skew results in ways that are hard to predict."},uuid:"3|2"},$R[471]={content:$R[472]={type:"header",text:"The Socially Acceptable Answer"},uuid:"3|3"},$R[473]={content:$R[474]={type:"text",text:"Most people want to be seen in a positive light. When asked a question, especially on a sensitive topic like politics or race, they might give an answer that makes them sound good, tolerant, or informed, rather than their true opinion. This is called social desirability bias."},uuid:"3|4"},$R[475]={content:$R[476]={type:"definition",term:"Social desirability bias",definition:"The tendency for survey respondents to answer questions in a way that will be viewed favorably by others, whether consciously or unconsciously.",syllables:$R[477]=["so","cial","de","sir","a","bil","i","ty","bi","as"],phonetic:"/ˈsoʊʃəl dɪˌzaɪərəˈbɪləti ˈbaɪəs/",partOfSpeech:"noun",exampleUsage:"Social desirability bias can cause polls to underestimate support for controversial policies."},uuid:"3|5"},$R[478]={content:$R[479]={type:"text",text:"A classic example of this bias in action is the Bradley effect. In 1982, Tom Bradley, a Black man, was running for governor of California against George Deukmejian, a white man. Pre-election polls consistently showed Bradley with a significant lead. But on election day, he lost.\n\nThe theory is that a number of white voters told pollsters they planned to vote for Bradley because they didn't want to appear prejudiced. In the privacy of the voting booth, however, they voted for his opponent. This phenomenon, where a non-white candidate's support is inflated in polls, was named after him."},uuid:"3|6"},$R[480]={content:$R[481]={type:"blockquoteWithCitation",text:"It has been argued that poll respondents may tend to mask their true political preference in favour of what is generally considered more socially acceptable.",assetId:3183859},uuid:"3|7"},$R[482]={content:$R[483]={type:"text",text:"Whether it’s called the Bradley effect or the “shy voter” theory, this concept continues to be debated in modern elections. Analysts have suggested it might help explain polling misses related to support for candidates like Donald Trump or for political movements like Brexit in the United Kingdom."},uuid:"3|8"},$R[484]={content:$R[485]={type:"header",text:"The Pollster's Own Influence"},uuid:"3|9"},$R[486]={content:$R[487]={type:"text",text:"Polling firms aren't identical. Each organization has its own unique recipe for conducting a poll. These methodological quirks, from how they contact people to how they adjust their data, can create a consistent lean in their results. This is known as a house effect."},uuid:"3|10"},$R[488]={content:$R[489]={type:"definition",term:"House effect",definition:"The tendency for a specific polling organization's results to consistently differ from the average of other polls, often due to its unique methodology.",syllables:$R[490]=["house","ef","fect"],phonetic:"/haʊs ɪˈfɛkt/",partOfSpeech:"noun",exampleUsage:"The poll from that firm always shows Democrats doing slightly better, which is likely due to their house effect."},uuid:"3|11"},$R[491]={content:$R[492]={type:"text",text:"House effects aren't necessarily a sign of intentional bias. They arise from the countless small decisions pollsters must make. For example:\n\n* **Likely Voter Screens:** How does a pollster decide if someone is actually going to vote? Some ask about past voting history, while others ask about enthusiasm for the current election. Different screens produce different electorates.\n* **Weighting:** As we've covered, pollsters weight their data to match demographics like age, race, and gender. But they might also weight by education level, party affiliation, or region. How heavily they weight each factor can nudge the final numbers.\n* **Mode of Interview:** A poll conducted with live telephone interviewers might get different results than an automated robocall or an online survey. The presence of a human interviewer can amplify social desirability bias."},uuid:"3|12"},$R[493]={content:$R[494]={type:"header",text:"Spotting and Mitigating Bias"},uuid:"3|13"},$R[495]={content:$R[496]={type:"text",text:"For the average person, identifying these biases in a single poll is difficult, if not impossible. But there are strategies you can use to get a clearer picture."},uuid:"3|14"},$R[497]={content:$R[498]={type:"text",text:"The most effective strategy is to look at polling averages. Websites that aggregate polls, like FiveThirtyEight or RealClearPolitics, gather results from many different firms. By averaging them, the unique house effect of any single poll gets smoothed out, giving you a more stable and reliable estimate of public opinion."},uuid:"3|15"},$R[499]={content:$R[500]={type:"blockquoteWithCitation",text:"Polling aggregators – sites that compile and average the results of many polls – can help us understand what the public prefers.",assetId:3245736},uuid:"3|16"},$R[501]={content:$R[502]={type:"text",text:"Another key is transparency. Reputable polling organizations are open about their methods. They will release information about who they surveyed, how they did it, the exact wording of their questions, and how they weighted the data. This information, often called the poll's methodology or "},uuid:"3|17"},$R[503]={content:$R[504]={type:"blockquote",text:"A good poll will publish its methodology, the proportion of cell phones to landlines called, its margin of error, its response rate."},uuid:"3|18"},$R[505]={content:$R[506]={type:"text",text:"If a poll doesn't share these details, it’s a major red flag. While these biases can never be eliminated entirely, understanding them allows you to consume poll results with a healthy dose of critical thinking."},uuid:"3|19"},$R[507]={content:$R[508]={type:"text",text:"Time to check what you've learned."},uuid:"3|20"},$R[509]={content:$R[510]={type:"quiz",questions:$R[511]=[$R[512]={text:"What is the primary difference between response bias and sampling bias in polling?",options:$R[513]=[$R[514]={text:"There is no significant difference; the terms are used interchangeably.",followup:"Incorrect. These are distinct concepts in polling analysis. Sampling bias is about the group selected, and response bias is about their answers.",isRightAnswer:!1},$R[515]={text:"Response bias occurs in online polls, while sampling bias occurs in telephone polls.",followup:"Incorrect. Both types of bias can occur regardless of the polling method.",isRightAnswer:!1},$R[516]={text:"Response bias relates to the sample size, while sampling bias relates to the margin of error.",followup:"Incorrect. Sampling bias is about the composition of the sample, not just its size. Response bias is about the content of the answers.",isRightAnswer:!1},$R[517]={text:"Response bias is about the accuracy of the answers given, while sampling bias is about who is included in the poll.",followup:"Correct. Response bias deals with what people say, whereas sampling bias deals with who gets asked.",isRightAnswer:!0}]},$R[518]={text:"A voter tells a pollster they support a certain environmental policy because they don't want to seem unconcerned about climate change, even though they privately disagree with it. This is a classic example of:",options:$R[519]=[$R[520]={text:"Social desirability bias",followup:"Correct. Social desirability bias is the tendency for poll respondents to answer questions in a way that will be viewed favorably by others.",isRightAnswer:!0},$R[521]={text:"A house effect",followup:"Incorrect. A house effect relates to a polling firm's specific methodology causing a consistent lean in their results.",isRightAnswer:!1},$R[522]={text:"Nonresponse bias",followup:"Incorrect. Nonresponse bias occurs when the people who choose not to participate in a poll are systematically different from those who do.",isRightAnswer:!1},$R[523]={text:"A likely voter screen",followup:"Incorrect. A likely voter screen is a tool used by pollsters to determine who is most likely to vote, which can be part of a house effect.",isRightAnswer:!1}]},$R[524]={text:"A polling organization's results consistently lean slightly more Democratic than other polls, regardless of the specific election. This is most likely due to a phenomenon known as the ____________.",options:$R[525]=[$R[526]={text:"House Effect",followup:"Correct. A house effect is a persistent statistical lean in a pollster's results caused by their unique methodology.",isRightAnswer:!0},$R[527]={text:"Bradley Effect",followup:"Incorrect. The Bradley Effect is a specific type of social desirability bias related to race and voting.",isRightAnswer:!1},$R[528]={text:"Margin of Error",followup:"Incorrect. The margin of error is a measure of sampling error, not a persistent methodological bias.",isRightAnswer:!1}]},$R[529]={text:"The 'Bradley effect' theorizes that Tom Bradley lost his 1982 election because polls were skewed by voters who were dishonest with pollsters about their intention to vote for a Black candidate.",options:$R[530]=[$R[531]={text:"True",followup:"That's right. The theory suggests that some white voters told pollsters they would vote for Bradley to avoid appearing prejudiced, but then voted for his opponent in private.",isRightAnswer:!0},$R[532]={text:"False",followup:"Incorrect. This is the core of the theory known as the Bradley effect, a specific example of social desirability bias.",isRightAnswer:!1}]},$R[533]={text:"According to the text, what is the MOST effective strategy for an average person to mitigate the impact of house effects and other response biases when evaluating polls?",options:$R[534]=[$R[535]={text:"Focus only on polls with a very small margin of error.",followup:"Incorrect. A low margin of error is good, but it only accounts for sampling error, not response biases like house effects.",isRightAnswer:!1},$R[536]={text:"Only trust polls conducted via live telephone interviews.",followup:"Incorrect. Live interviewers can sometimes increase social desirability bias. No single method is perfect.",isRightAnswer:!1},$R[537]={text:"Look at polling averages from multiple different firms.",followup:"Correct. By averaging many polls, the unique biases of any single firm tend to be smoothed out, providing a more reliable picture.",isRightAnswer:!0},$R[538]={text:"Try to contact the polling firm directly to ask about their biases.",followup:"Incorrect. While transparency is key, a more effective and practical strategy is to look at averages from aggregators.",isRightAnswer:!1}]}]},uuid:"3|21"}]},$R[539]={uuid:"4",title:"Analyzing Polling Methodologies",includesKnowledgeBase:!1,hasDemonstratedMastery:!1,streaming:!1,blocks:$R[540]=[$R[541]={content:$R[542]={type:"header",text:"Behind the Numbers"},uuid:"4|0"},$R[543]={content:$R[544]={type:"text",text:"Two polls can ask about the same election and get very different results. One might show a candidate leading by five points, while another shows them trailing. How is this possible? The answer lies in the methodology—the specific choices a pollster makes when conducting their survey.\n\nLooking past the headline numbers to understand *how* a poll was conducted is the key to telling a good poll from a bad one. Three of the most important factors are the sampling frame, the wording of the questions, and how the data is weighted."},uuid:"4|1"},$R[545]={content:$R[546]={type:"header",text:"Who Are They Asking?"},uuid:"4|2"},$R[547]={content:$R[548]={type:"text",text:"Before a pollster can survey a sample of the population, they need a list of that population to draw from. This list is called a **sampling frame**. The quality of this frame is crucial. If it's incomplete or biased, the poll results will be, too.\n\nDifferent organizations use different sampling frames, each with its own trade-offs."},uuid:"4|3"},$R[549]={content:$R[550]={type:"table",markdown:"| Frame Type | How It Works | Pros | Cons |\n| :--- | :--- | :--- | :--- |\n| **Voter Files** | Lists of registered voters from state or county records. | Targets actual voters. Rich in demographic data. | Can be outdated. Misses newly registered voters. |\n| **Random Digit Dialing** | Computers randomly generate phone numbers (landline & cell). | Can reach almost any household with a phone. | Very low response rates. Skips people without phones. |\n| **Address-Based** | A list of all residential mailing addresses from the USPS. | Reaches nearly all households. Good for local races. | Expensive and slower. Initial contact is by mail. |\n| **Online Panels** | A pre-recruited group of people who agree to take surveys. | Fast and cheap. | Not truly random. Can attract 'professional' survey-takers. |"},uuid:"4|4"},$R[551]={content:$R[552]={type:"text",text:"A high-quality polling firm will be transparent about its sampling frame. If they use an online panel, for example, they should explain how they recruit participants and correct for potential biases. If a pollster doesn't share this information, it's a red flag."},uuid:"4|5"},$R[553]={content:$R[554]={type:"blockquoteWithCitation",text:"If a pollster isn’t revealing its methodology, don’t trust it.",assetId:3245542},uuid:"4|6"},$R[555]={content:$R[556]={type:"header",text:"The Art of the Question"},uuid:"4|7"},$R[557]={content:$R[558]={type:"text",text:"The way a question is phrased can steer respondents toward a certain answer. Even small changes in wording can lead to big swings in the results. This isn't just about avoiding obvious bias; it's about crafting neutral, clear questions that everyone understands in the same way."},uuid:"4|8"},$R[559]={content:$R[560]={type:"blockquote",text:"Consider two ways to ask about government spending:\n\n1. Are we spending too much, too little, or about the right amount on **'welfare'**?\n2. Are we spending too much, too little, or about the right amount on **'assistance to the poor'**?\n\nMany more people support spending on 'assistance to the poor' than on 'welfare', even though the terms can refer to the same programs. The word 'welfare' carries negative connotations for some people, influencing their response."},uuid:"4|9"},$R[561]={content:$R[562]={type:"text",text:"The order of questions also matters. If you ask people about their financial struggles right before asking them to rate the president's job performance, you might get a more negative rating than if you asked those questions in the reverse order. Reputable pollsters carefully design their questionnaires to minimize these effects and often test different question wordings."},uuid:"4|10"},$R[563]={content:$R[564]={type:"realImage",url:"https://oboe-storage.s3.amazonaws.com/dev/imagesReal/v1/c979ca86-fc38-4af0-b2ff-af137ab420eb.jpeg",attributionUrl:"https://commons.wikimedia.org/wiki/File:Multi-item_psychometric_scale.jpg",caption:"Pollsters use psychometric scales to measure opinions, where both the question and the response options are carefully designed."},uuid:"4|11"},$R[565]={content:$R[566]={type:"header",text:"Adjusting the Scales"},uuid:"4|12"},$R[567]={content:$R[568]={type:"text",text:"No poll sample is a perfect mirror of the population. Some groups are always harder to reach than others. Young people and minorities, for instance, are often underrepresented in survey samples, while older, more educated people might be overrepresented.\n\nTo fix this, pollsters use a statistical technique called **weighting**. They adjust the results so that the demographic makeup of their sample matches the demographics of the population they are trying to measure (like all registered voters). For example, if a state's population is 15% Hispanic but a poll's sample is only 10% Hispanic, the pollster will give more weight to the answers from Hispanic respondents to make up for the shortfall."},uuid:"4|13"},$R[569]={content:$R[570]={type:"definition",term:"Weighting",definition:"A statistical adjustment made to survey data to ensure the sample accurately reflects the demographic characteristics of the target population.",syllables:$R[571]=["weight","ing"],phonetic:"/ˈweɪtɪŋ/",partOfSpeech:"noun",exampleUsage:"The poll used weighting to correct for an oversampling of older voters."},uuid:"4|14"},$R[572]={content:$R[573]={type:"text",text:"Weighting is a standard and necessary part of modern polling, but it can also be a source of error. Different pollsters might weight for different variables—like age, race, gender, education, and geographic region. Some even weight by party identification or past voting behavior.\n\nHow a pollster weights their data can have a significant impact on the final numbers. That's why transparency is so important. Good polls explain which variables they used for weighting and how they did it."},uuid:"4|15"},$R[574]={content:$R[575]={type:"text",text:"Ready to test your ability to spot sound polling methods?"},uuid:"4|16"},$R[576]={content:$R[577]={type:"quiz",questions:$R[578]=[$R[579]={text:"What is a \"sampling frame\" in the context of polling?",options:$R[580]=[$R[581]={text:"The physical location where the poll is conducted.",followup:"The sampling frame is a list of people, not a place.",isRightAnswer:!1},$R[582]={text:"The list of the population from which a poll's sample is drawn.",followup:"Correct. The sampling frame is the source list (like a list of registered voters) used to select potential respondents.",isRightAnswer:!0},$R[583]={text:"The final set of people who actually respond to the poll.",followup:"This describes the final sample, not the list from which the sample was chosen.",isRightAnswer:!1},$R[584]={text:"The demographic breakdown of the poll's results.",followup:"This is a description of the poll's demographics, not the initial list used for sampling.",isRightAnswer:!1}]},$R[585]={text:"Why do pollsters use a statistical technique called \"weighting\"?",options:$R[586]=[$R[587]={text:"To make the poll's questions easier for people to understand.",followup:"Question design, not weighting, is focused on clarity and neutrality.",isRightAnswer:!1},$R[588]={text:"To increase the total number of respondents in their poll.",followup:"Weighting adjusts the influence of existing responses; it doesn't add new ones.",isRightAnswer:!1},$R[589]={text:"To ensure the poll results are always favorable to a specific candidate.",followup:"This would be an unethical misuse of polling techniques. Legitimate weighting aims for accuracy, not bias.",isRightAnswer:!1},$R[590]={text:"To adjust the sample's demographic makeup to better match the target population.",followup:"Correct. Weighting corrects for overrepresentation or underrepresentation of certain groups in the sample.",isRightAnswer:!0}]},$R[591]={text:"A pollster finds their sample contains a higher percentage of college-educated respondents than the general population of registered voters. What is the most appropriate action to take?",options:$R[592]=[$R[593]={text:"Publish the results as-is, but mention the sample is more educated.",followup:"While transparent, this doesn't fix the underlying issue, and the headline numbers would likely be skewed.",isRightAnswer:!1},$R[594]={text:"Apply a statistical weight to the data to correct for the overrepresentation.",followup:"Correct. Weighting is the standard procedure to adjust the sample so its demographics align with the target population.",isRightAnswer:!0},$R[595]={text:"Remove all college-educated respondents from the results.",followup:"This is too extreme and would eliminate a valid demographic group from the poll entirely.",isRightAnswer:!1},$R[596]={text:"Conduct a completely new poll from scratch.",followup:"This is usually impractical and unnecessary, as weighting is designed to solve this exact problem.",isRightAnswer:!1}]},$R[597]={text:"Which of the following questions is worded most neutrally for a poll about a proposed tax increase?",options:$R[598]=[$R[599]={text:"Do you support the new tax that will fund essential community services?",followup:"This wording is biased because it highlights only the positive outcomes of the tax.",isRightAnswer:!1},$R[600]={text:"Do you oppose the burdensome new tax that will take money from hardworking families?",followup:"This question uses emotionally charged, negative language to steer respondents toward opposition.",isRightAnswer:!1},$R[601]={text:"Do you favor or oppose the proposed increase in the local sales tax?",followup:"Correct. This question presents the issue and the options (favor/oppose) in a clear, neutral manner without leading language.",isRightAnswer:!0}]},$R[602]={text:"A reputable polling firm will be transparent about its methodology, including its sampling frame and how it weights data.",options:$R[603]=[$R[604]={text:"True",followup:"Correct. Transparency about methodology is a key sign of a high-quality, trustworthy poll.",isRightAnswer:!0},$R[605]={text:"False",followup:"Incorrect. Pollsters who hide their methods are considered less credible because their results cannot be independently evaluated.",isRightAnswer:!1}]}]},uuid:"4|17"},$R[606]={content:$R[607]={type:"text",text:"By understanding these methodological details, you can look beyond the headlines and make your own informed judgments about a poll's credibility."},uuid:"4|18"}]},$R[608]={uuid:"5",title:"Interpreting Poll Aggregates and Forecasts",includesKnowledgeBase:!1,hasDemonstratedMastery:!1,streaming:!1,blocks:$R[609]=[$R[610]={content:$R[611]={type:"header",text:"The Wisdom of Crowds"},uuid:"5|0"},$R[612]={content:$R[613]={type:"text",text:"A single poll can be a useful snapshot, but it can also be an outlier. One survey might catch a fleeting shift in public mood, while another might have a slightly skewed sample. Relying on just one poll is like trying to understand a whole movie by looking at a single frame. You get a piece of the story, but you miss the bigger picture."},uuid:"5|1"},$R[614]={content:$R[615]={type:"text",text:"This is where poll aggregation comes in. Instead of focusing on individual polls, aggregators combine the results from many different surveys to create a more stable and reliable estimate of public opinion. Think of it as seeking a second, third, and fourth opinion. This approach, famously used by sites like FiveThirtyEight and The Economist, helps to smooth out the noise from individual polls."},uuid:"5|2"},$R[616]={content:$R[617]={type:"blockquoteWithCitation",text:"Don’t just look at one poll, look at a poll aggregator.",assetId:2799994},uuid:"5|3"},$R[618]={content:$R[619]={type:"header",text:"How Aggregators Work"},uuid:"5|4"},$R[620]={content:$R[621]={type:"text",text:"At its simplest, aggregation is just averaging. But sophisticated aggregators don't treat all polls equally. They use a weighted average, giving more influence to polls they deem more reliable. Not all polls are created equal, and the weighting reflects that."},uuid:"5|5"},$R[622]={content:$R[623]={type:"text",text:"Several factors typically go into this weighting process:\n\n* **Sample Size:** A poll with 2,000 respondents is generally given more weight than one with 500, because larger samples tend to have a smaller margin of error.\n* **Recency:** Public opinion can change quickly. A poll conducted yesterday is usually more relevant than one from last month, so it gets more weight.\n* **Pollster Quality:** Aggregators rate polling organizations based on their historical accuracy and methodological transparency. A pollster with a long track record of accurate results will be weighted more heavily than a newcomer or one with a history of bias."},uuid:"5|6"},$R[624]={content:$R[625]={type:"blockquoteWithCitation",text:"Polls are weighted based on their sample size, their recency and their pollster rating (which in turn is based on the past accuracy of the pollster, as well as its methodology).",assetId:3245573},uuid:"5|7"},$R[626]={content:$R[627]={type:"header",text:"From Aggregates to Forecasts"},uuid:"5|9"},$R[628]={content:$R[629]={type:"text",text:"Poll aggregates give us the best possible picture of public opinion *right now*. A forecast tries to use that picture to predict what will happen on Election Day. This is a crucial distinction. An aggregate is a summary of past data, while a forecast is a projection into the future."},uuid:"5|10"},$R[630]={content:$R[631]={type:"text",text:"Forecasting models take the poll aggregate and add other layers of information. They might account for:\n\n* **Economic Conditions:** Is the economy strong or weak?\n* **Historical Trends:** How have similar elections played out in the past?\n* **Expert Ratings:** How do non-partisan analysts rate the race?\n* **Demographic Shifts:** How is the electorate changing?"},uuid:"5|11"},$R[632]={content:$R[633]={type:"text",text:"The model then runs thousands of simulations to determine the range of possible outcomes. That’s why you’ll see forecasts presented as probabilities, like “Candidate A has a 75% chance of winning.” This doesn’t mean the candidate has 75% of the vote. It means that in 7,500 out of 10,000 simulations of the election, Candidate A came out on top."},uuid:"5|12"},$R[634]={content:$R[635]={type:"realImage",url:"https://oboe-storage.s3.amazonaws.com/dev/imagesReal/v1/4a931b2d-f0ca-4adc-90a6-78157b8c1347.png",attributionUrl:"https://commons.wikimedia.org/wiki/File:CoalitionDemo.png",caption:"Forecasting models run simulations to estimate the probability of different outcomes."},uuid:"5|13"},$R[636]={content:$R[637]={type:"header",text:"Not a Crystal Ball"},uuid:"5|14"},$R[638]={content:$R[639]={type:"text",text:"While powerful, aggregation and forecasting have limitations. They are tools for understanding probability, not for predicting the future with certainty. A 10% chance of an upset is not a 0% chance. Unexpected events, shifts in voter turnout, or systematic polling errors can all lead to surprising results."},uuid:"5|15"},$R[640]={content:$R[641]={type:"text",text:"The misses in the 2016 and 2020 U.S. presidential elections are famous examples. Many forecasts showed Hillary Clinton and Joe Biden with strong leads, respectively, but the final margins were much tighter. This wasn’t necessarily because the polls were “wrong,” but because the models may have underestimated the possibility of a systemic error—where many polls were off in the same direction—and the level of uncertainty in the race."},uuid:"5|16"},$R[642]={content:$R[643]={type:"blockquote",text:"The goal of a forecast is not to be perfectly right, but to accurately quantify uncertainty. It tells you the range of what could happen, not what will happen."},uuid:"5|17"},$R[644]={content:$R[645]={type:"text",text:"By combining multiple sources and modeling uncertainty, poll aggregates and forecasts offer a much more nuanced view than any single survey can. They help us see the trends, understand the probabilities, and appreciate the complexities of an election."},uuid:"5|18"},$R[646]={content:$R[647]={type:"quiz",questions:$R[648]=[$R[649]={text:"What is the primary advantage of poll aggregation over relying on a single poll?",options:$R[650]=[$R[651]={text:"It provides a more stable and reliable estimate by smoothing out the noise and potential errors from individual polls.",followup:"Correct! By combining many polls, aggregation helps to cancel out the random errors and biases of any single survey, giving a clearer picture of public opinion.",isRightAnswer:!0},$R[652]={text:"It exclusively uses polls from the most famous and well-known media outlets.",followup:"Sophisticated aggregators weight polls based on their historical accuracy and methods, not just their fame.",isRightAnswer:!1},$R[653]={text:"It is a much faster method for gathering public opinion.",followup:"Aggregation is the process of combining existing polls, so it depends on the speed at which individual polls are conducted and released. The process itself adds a step.",isRightAnswer:!1},$R[654]={text:"It guarantees a perfectly accurate prediction of the election outcome.",followup:"Aggregation improves reliability but doesn't guarantee accuracy. Forecasts are about probabilities, not certainties.",isRightAnswer:!1}]},$R[655]={text:"When creating a weighted average, which of the following factors would likely cause a poll aggregator to give a particular poll *less* weight?",options:$R[656]=[$R[657]={text:"The poll has a very large sample size.",followup:"A larger sample size typically means a smaller margin of error, so these polls are usually given more weight.",isRightAnswer:!1},$R[658]={text:"The poll was conducted very recently.",followup:"More recent polls are generally given more weight because they reflect the most current public opinion.",isRightAnswer:!1},$R[659]={text:"The polling organization has a poor track record of accuracy.",followup:"That's right. Pollster quality, based on historical accuracy and methodological transparency, is a key factor. A pollster with a history of bias or inaccuracy would be down-weighted.",isRightAnswer:!0},$R[660]={text:"The poll's methodology is transparent and publicly available.",followup:"Methodological transparency is a sign of a high-quality poll, which would typically lead to it being given more, not less, weight.",isRightAnswer:!1}]},$R[661]={text:"What is the key difference between a poll aggregate and an election forecast?",options:$R[662]=[$R[663]={text:"There is no real difference; the terms are used interchangeably.",followup:"This is a common misconception. An aggregate is a summary of past data, while a forecast is a projection into the future. They are distinct concepts.",isRightAnswer:!1},$R[664]={text:"A forecast is presented as a percentage of the vote, while an aggregate is presented as a probability.",followup:"It's the other way around. Forecasts are often presented as probabilities (e.g., 'a 75% chance to win'), while aggregates show the current estimated vote share.",isRightAnswer:!1},$R[665]={text:"An aggregate summarizes current polling data, while a forecast uses that data and other factors to predict a future outcome.",followup:"Exactly! An aggregate tells you where public opinion is right now based on all available polls. A forecast tries to predict where it will be on Election Day.",isRightAnswer:!0},$R[666]={text:"An aggregate is a simple average of polls, while a forecast is a weighted average.",followup:"Sophisticated aggregates use a weighted average, too. The key distinction is between summarizing the present and predicting the future.",isRightAnswer:!1}]},$R[667]={text:"An election forecast states that 'Candidate A has an 80% chance of winning.' What is the correct interpretation of this statement?",options:$R[668]=[$R[669]={text:"80% of all polls currently show Candidate A in the lead.",followup:"This describes a poll aggregate, not a forecast. A forecast incorporates the aggregate but also adds other variables to project a future outcome.",isRightAnswer:!1},$R[670]={text:"There is only a 20% chance that the forecast is wrong.",followup:"The 20% chance represents the probability of the opponent winning, not the probability of the model itself being incorrect.",isRightAnswer:!1},$R[671]={text:"Candidate A is expected to receive 80% of the total votes on Election Day.",followup:"This is a common misinterpretation. The percentage refers to the probability of winning the election, not the share of the vote.",isRightAnswer:!1},$R[672]={text:"If the election were simulated thousands of times based on the model's data, Candidate A would win in about 80% of those simulations.",followup:"Correct! The percentage represents the model's confidence in an outcome, based on running many simulations with the available data. It's a statement of probability, not certainty.",isRightAnswer:!0}]},$R[673]={text:"True or False: The failures of some polls to predict the narrow margins of the 2016 and 2020 U.S. presidential elections prove that aggregation and forecasting are fundamentally useless.",options:$R[674]=[$R[675]={text:"True",followup:"While those elections highlighted the limitations, such as underestimating systemic polling errors, these tools still provide a more nuanced and powerful view than any single poll. They are tools for understanding probability, not crystal balls.",isRightAnswer:!1},$R[676]={text:"False",followup:"Correct. Those election outcomes highlighted the limitations of forecasting, especially the possibility of systemic polling errors where many polls are off in the same direction. However, they don't render the entire approach useless; they emphasize that a 10% or 20% chance of an upset is a real possibility.",isRightAnswer:!0}]}]},uuid:"5|19"}]},$R[677]={uuid:"6",title:"Case Studies of Polling Inaccuracies",includesKnowledgeBase:!1,hasDemonstratedMastery:!1,streaming:!1,blocks:$R[678]=[$R[679]={content:$R[680]={type:"header",text:"When Polls Get It Wrong"},uuid:"6|0"},$R[681]={content:$R[682]={type:"text",text:"Election polls aim to capture a snapshot of public opinion. But sometimes, that snapshot is out of focus. High-profile misses can shake public confidence and leave experts scrambling for answers. Examining these failures isn't about discrediting polling, but about understanding its limitations and complexities. Two of the most significant recent examples are the 2016 and 2020 U.S. presidential elections."},uuid:"6|1"},$R[683]={content:$R[684]={type:"header",text:"The 2016 U.S. Election Shock"},uuid:"6|2"},$R[685]={content:$R[686]={type:"text",text:"Leading up to Election Day in 2016, the vast majority of national and state-level polls pointed to a victory for Hillary Clinton. Some forecasting models gave her over a 90% chance of winning. The actual result, a victory for Donald Trump, was a stunning surprise that prompted a period of intense soul-searching within the polling industry."},uuid:"6|3"},$R[687]={content:$R[688]={type:"realImage",url:"https://oboe-storage.s3.amazonaws.com/dev/imagesReal/v1/course-59498/f25ab325-c4bf-4763-a7bb-736e8e6be465.jpeg",attributionUrl:"https://commons.wikimedia.org/wiki/File:End_Of_The_Line_For_2016_Elections_(183152081).jpeg",caption:"Students watch as election results come in, a scene of surprise for many across the country in 2016."},uuid:"6|4"},$R[689]={content:$R[690]={type:"text",text:"So, what happened? Several factors contributed to the discrepancy. One of the most significant was an underestimation of support for Donald Trump among specific demographic groups. Many state polls failed to properly weight their samples by education level. Voters without a college degree, a key demographic for Trump, were underrepresented in the surveys. This oversight proved critical in Rust Belt states like Pennsylvania, Michigan, and Wisconsin, where Trump won by razor-thin margins.\n\nAnother factor was the behavior of undecided voters. A larger-than-usual share of voters made their final decision in the last week of the campaign, and they broke decisively for Trump. This late shift wasn't fully captured by the final polls."},uuid:"6|5"},$R[691]={content:$R[692]={type:"blockquote",text:"The 2016 election revealed critical blind spots in polling, especially the failure to account for education levels in weighting and the last-minute decisions of undecided voters."},uuid:"6|6"},$R[693]={content:$R[694]={type:"header",text:"A Different Miss in 2020"},uuid:"6|7"},$R[695]={content:$R[696]={type:"text",text:"After the 2016 election, pollsters worked to correct their mistakes. They adjusted weighting methods and paid closer attention to voter demographics. Heading into 2020, most polls again predicted a victory for the Democratic candidate, Joe Biden. This time, they correctly predicted the winner.\n\nHowever, they were wrong again about the margin. Polls suggested a comfortable, perhaps even landslide, win for Biden. The final result was much closer. The national popular vote was off by several percentage points, and polls in key battleground states like Wisconsin and Florida overestimated Biden's support significantly."},uuid:"6|8"},$R[697]={content:$R[698]={type:"realImage",url:"https://oboe-storage.s3.amazonaws.com/dev/imagesReal/v1/9fbb0268-e8bc-49fa-a015-45c19e2b12c5.png",attributionUrl:"https://commons.wikimedia.org/wiki/File:US_2020_Election_Vote_Change_by_State.png",caption:"A map shows how vote margins shifted between the 2016 and 2020 U.S. presidential elections."},uuid:"6|9"},$R[699]={content:$R[700]={type:"text",text:"The 2020 errors were more systemic and harder to explain than those in 2016. It wasn't just one demographic that was miscounted. The leading theory points to a specific kind of nonresponse bias. For various reasons, including a growing distrust of institutions, some Republican voters may have been systematically less likely to respond to pollsters than Democratic voters. This wasn't a case of voters being dishonest about their choice (the so-called \"shy Trump voter\" theory), but rather of them refusing to participate in polls at all.\n\nThe unique circumstances of the COVID-19 pandemic may have also played a role, altering how and when pollsters could reach people and potentially skewing who was available and willing to answer."},uuid:"6|10"},$R[701]={content:$R[702]={type:"blockquoteWithCitation",text:"With historic misses in the 2016 and 2020 US Presidential elections, interest in measuring polling errors has increased.",assetId:3243922},uuid:"6|11"},$R[703]={content:$R[704]={type:"text",text:"These case studies highlight an important lesson: polling is a science, but not a perfect one. It relies on statistical models of human behavior, which is notoriously difficult to predict. Factors like who decides to vote, who responds to polls, and last-minute events can all introduce uncertainty. This doesn't mean polls are useless, but it does mean they should be interpreted with a healthy dose of skepticism and a clear understanding of their potential flaws."},uuid:"6|12"}]},$R[705]={uuid:"7",title:"Applying Critical Interpretation Skills",includesKnowledgeBase:!1,hasDemonstratedMastery:!1,streaming:!1,blocks:$R[706]=[$R[707]={content:$R[708]={type:"header",text:"Putting It All Together"},uuid:"7|0"},$R[709]={content:$R[710]={type:"text",text:"You've learned the building blocks of polling: sampling, margin of error, confidence intervals, and the various biases that can creep into survey data. Now, it's time to move from theory to practice. Interpreting a poll isn't about accepting its headline number. It's about becoming a detective, piecing together clues from the methodology to assess its reliability."},uuid:"7|1"},$R[711]={content:$R[712]={type:"text",text:"This final step is about synthesis. You'll learn to combine your knowledge of sampling methods, statistical concepts, and potential biases to form a holistic view of a poll's findings. The goal is to develop a reflex for critical evaluation, so you can confidently judge the quality of the data you encounter."},uuid:"7|2"},$R[713]={content:$R[714]={type:"blockquoteWithCitation",text:"To get the full picture, examine more than one poll.",assetId:3245754},uuid:"7|3"},$R[715]={content:$R[716]={type:"header",text:"A Checklist for Critical Evaluation"},uuid:"7|4"},$R[717]={content:$R[718]={type:"text",text:"When you see a new poll, it’s easy to focus on the top-line numbers. Who’s ahead? By how much? But the real story is often buried in the methodology section. Think of yourself as an inspector. Before you trust the results, run through a mental checklist to scrutinize the details."},uuid:"7|5"},$R[719]={content:$R[720]={type:"text",text:"Here are the key questions you should ask every time you encounter a poll. The answers will help you decide how much weight to give its conclusions."},uuid:"7|6"},$R[721]={content:$R[722]={type:"table",markdown:"| Question | Why It Matters |\n|---|---|\n| **Who conducted the poll?** | Look for the pollster's reputation and potential house effects. Is it a non-partisan university or a firm with a known political leaning? |\n| **Who was surveyed?** | \"Likely voters\" are the gold standard for election polls. Polls of \"registered voters\" or \"all adults\" are less predictive of election outcomes. |\n| **What was the sample size?** | A larger sample size generally leads to a smaller margin of error, but size isn't everything. A large, biased sample is still a biased sample. |\n| **What's the margin of error?** | If a candidate's lead is within the margin of error, the race is statistically a toss-up. Don't mistake a small lead for a certain one. |\n| **How was the poll conducted?** | Live-caller, online, or automated (IVR) polls have different strengths and can attract different types of respondents, affecting potential biases. |\n| **How was the data weighted?** | Weighting adjusts the sample to better match the demographics of the target population (e.g., age, race, gender). Reputable polls are transparent about this. |\n| **What were the exact questions?** | Leading or loaded questions can significantly skew results. The wording matters immensely. |"},uuid:"7|7"},$R[723]={content:$R[724]={type:"text",text:"Treat this checklist as your foundational tool. It shifts your focus from a poll's conclusion to its construction, which is where its true strength or weakness lies."},uuid:"7|8"},$R[725]={content:$R[726]={type:"header",text:"Spotting Bias in the Wild"},uuid:"7|9"},$R[727]={content:$R[728]={type:"text",text:"Identifying bias isn't always straightforward. It often requires comparing multiple polls and looking for patterns. Imagine two polls for the same mayoral race are released on the same day.\n\n**Poll A** shows Candidate Smith ahead by 8 points (49% to 41%). It's an online poll of 1,200 registered voters.\n\n**Poll B** shows Candidate Jones ahead by 1 point (46% to 45%). It's a live-caller poll of 600 likely voters."},uuid:"7|10"},$R[729]={content:$R[730]={type:"text",text:"At first glance, the results seem contradictory. But using your checklist, you can start to understand why they differ. Poll A has a larger sample, but its universe of \"registered voters\" is less precise than Poll B's \"likely voters.\" Online polls might also attract a more technologically savvy, and potentially younger, demographic that needs to be heavily weighted to be representative.\n\nPoll B's live-caller method might yield a more representative sample of older voters, but its smaller size means a larger margin of error. The one-point difference is well within that margin, making the race a statistical tie. Furthermore, if Candidate Smith has a controversial stance on a key issue, some respondents might be hesitant to admit their support to a live interviewer, an example of social desirability bias. They might be more honest in an anonymous online poll.\n\nNeither poll is necessarily \"wrong.\" They are different snapshots taken with different cameras and lenses. The key is to understand how their methodologies could lead to different outcomes."},uuid:"7|11"},$R[731]={content:$R[732]={type:"blockquote",text:"The most reliable insights often come from the trend, not a single poll. Look at the average of several high-quality polls to get a clearer picture of the political landscape."},uuid:"7|12"},$R[733]={content:$R[734]={type:"header",text:"From Data to Forecast"},uuid:"7|13"},$R[735]={content:$R[736]={type:"text",text:"Assessing the reliability of a forecast, like those from poll aggregators, requires an extra layer of analysis. These models don't just average polls; they weight them based on the pollster's historical accuracy, sample size, and methodology. They also incorporate other factors, like economic data or fundraising numbers."},uuid:"7|14"},$R[737]={content:$R[738]={type:"text",text:"When evaluating a forecast, consider these points:\n\n1. **Transparency:** Does the forecaster explain their methodology? They should be clear about which polls they include and how they weight them.\n2. **Uncertainty:** A good forecast expresses uncertainty. It will present outcomes as probabilities (e.g., a 75% chance of winning) rather than certainties. Be wary of any model that claims to know the future.\n3. **Sensitivity:** How much does the forecast change with new polls? A volatile model might be overreacting to daily polling noise, while a very stable one might be too slow to capture real shifts in public opinion."},uuid:"7|15"},$R[739]={content:$R[740]={type:"realImage",url:"https://oboe-storage.s3.amazonaws.com/dev/imagesReal/v1/fbcd2b5b-f062-483f-a160-f6f427f1e27e.jpeg",attributionUrl:"https://www.pexels.com/photo/a-group-of-business-partner-looking-at-a-graph-print-out-7693707/",caption:"Analyzing poll data requires careful examination of methodologies and trends."},uuid:"7|16"},$R[741]={content:$R[742]={type:"text",text:"Remember, a forecast is not a prediction. It's a statement of probability based on the available data at a specific moment in time. As the data changes, the forecast will too."},uuid:"7|17"},$R[743]={content:$R[744]={type:"text",text:"Time to put your skills to the test."},uuid:"7|18"},$R[745]={content:$R[746]={type:"quiz",questions:$R[747]=[$R[748]={text:"When comparing two polls on the same race, what is the most important first step in understanding why their results might differ?",options:$R[749]=[$R[750]={text:"Check which candidate is leading in each poll.",followup:"While this identifies the difference, it doesn't explain it. The methodology is where the explanation lies.",isRightAnswer:!1},$R[751]={text:"Assume the poll with the larger sample size is more accurate.",followup:"A larger sample size reduces the margin of error, but it doesn't guarantee a more representative sample or overcome other potential biases.",isRightAnswer:!1},$R[752]={text:"Examine the methodology of each poll, including the sample population and how respondents were contacted.",followup:"Correct. Differences in methodology—such as polling 'likely voters' vs. 'registered voters' or using live callers vs. online panels—are key to understanding different outcomes.",isRightAnswer:!0},$R[753]={text:"Average the results of the two polls to find the 'real' standing.",followup:"Simply averaging them ignores the different quality and methodologies of the polls. Professional aggregators use sophisticated weighting.",isRightAnswer:!1}]},$R[754]={text:"A pollster surveying a controversial issue finds that their online poll shows higher support for the controversial position than their live-caller poll. What type of bias could most likely explain this difference?",options:$R[755]=[$R[756]={text:"Social desirability bias",followup:"Correct. Respondents may be hesitant to admit an unpopular view to a live person but more willing to do so in an anonymous online format.",isRightAnswer:!0},$R[757]={text:"Selection bias",followup:"Selection bias might play a role if the online panel isn't representative, but it doesn't specifically address why people might answer differently based on the method.",isRightAnswer:!1},$R[758]={text:"Non-response bias",followup:"Non-response bias occurs when people who refuse to answer are systematically different from those who do, but it doesn't fully explain the difference between poll types.",isRightAnswer:!1},$R[759]={text:"Wording bias",followup:"This would be a possibility if the question wording were different in the two polls, but the scenario implies the difference is due to the polling method itself.",isRightAnswer:!1}]},$R[760]={text:"A good election forecast model should present its findings as certainties (e.g., 'Candidate X will win').",options:$R[761]=[$R[762]={text:"True",followup:"This is incorrect. A reliable forecast expresses uncertainty and presents outcomes as probabilities, not as certain predictions.",isRightAnswer:!1},$R[763]={text:"False",followup:"Correct. A good forecast model acknowledges uncertainty and presents outcomes as probabilities (e.g., a 75% chance of winning) based on the available data.",isRightAnswer:!0}]},$R[764]={text:"Which of the following is considered a more precise population for predicting an election's outcome?",options:$R[765]=[$R[766]={text:"Likely voters",followup:"Correct. Pollsters use screening questions to identify 'likely voters,' which provides a more targeted and often more accurate sample for election forecasting.",isRightAnswer:!0},$R[767]={text:"Registered voters",followup:"This is better, but many registered voters do not end up voting in a given election.",isRightAnswer:!1},$R[768]={text:"Previous voters",followup:"While past voting history is a component of a 'likely voter' screen, it doesn't capture new voters or those who vote intermittently.",isRightAnswer:!1},$R[769]={text:"All adults",followup:"This is too broad, as many adults are not registered to vote or do not plan to vote.",isRightAnswer:!1}]},$R[770]={text:"What does it mean if a poll aggregator's forecast is described as having high 'sensitivity'?",options:$R[771]=[$R[772]={text:"The forecast is very stable and doesn't change much.",followup:"This describes a model with low sensitivity, which might be too slow to react to real shifts.",isRightAnswer:!1},$R[773]={text:"The forecast model is highly transparent about its methods.",followup:"Transparency is a crucial but separate characteristic of a good forecasting model.",isRightAnswer:!1},$R[774]={text:"The forecast changes significantly in response to new polls.",followup:"Correct. High sensitivity means the model reacts strongly to new data, which can be a sign it is overreacting to daily polling noise.",isRightAnswer:!0}]}]},uuid:"7|19"},$R[775]={content:$R[776]={type:"text",text:"You've reached the end of the road. By consistently applying these critical interpretation skills, you can navigate the flood of election polling with confidence, separating the signal from the noise and becoming a more informed observer of the democratic process."},uuid:"7|20"}]}],version:3,formats:$R[777]=["deepdive"],isBookmarked:!1}},ssr:!0},$R[778]={i:"�_web�learn�$searchSlug��learn�decoding-election-polls-156svv1�",u:1784662080681,s:"success",l:$R[779]={courseData:$R[21]},ssr:!0}],lastMatchId:"�_web�learn�$searchSlug��learn�decoding-election-polls-156svv1�",dehydratedData:$R[780]={queryStream:$R[781]=($R[782]=(e) => new ReadableStream({ start: (r) => { e.on({ next: (a) => { try { r.enqueue(a); } catch (t) {} }, throw: (a) => { r.error(a); }, return: () => { try { r.close(); } catch (a) {} } }); } }))($R[783]=($R[784]=() => { let e = [], r = [], t = !0, n = !1, a = 0, s = (l, g, S) => { for (S = 0; S < a; S++) r[S] && r[S][g](l); }, i = (l, g, S, d) => { for (g = 0, S = e.length; g < S; g++) d = e[g], !t && g === S - 1 ? l[n ? "return" : "throw"](d) : l.next(d); }, u = (l, g) => (t && (g = a++, r[g] = l), i(l), () => { t && (r[g] = r[a], r[a--] = void 0); }); return { __SEROVAL_STREAM__: !0, on: (l) => u(l), next: (l) => { t && (e.push(l), s(l, "next")); }, throw: (l) => { t && (e.push(l), s(l, "throw"), t = !1, n = !1, r.length = 0); }, return: (l) => { t && (e.push(l), s(l, "return"), t = !1, n = !0, r.length = 0); } }; })()),dehydratedQueryClient:$R[785]={mutations:$R[786]=[],queries:$R[787]=[$R[788]={dehydratedAt:1784662080681,state:$R[789]={data:null,dataUpdateCount:1,dataUpdatedAt:1784662080622,error:null,errorUpdateCount:0,errorUpdatedAt:0,fetchFailureCount:0,fetchFailureReason:null,fetchMeta:null,isInvalidated:!1,status:"success",fetchStatus:"idle"},queryKey:$R[790]=["currentUser"],queryHash:"[\"currentUser\"]"},$R[791]={dehydratedAt:1784662080681,state:$R[792]={data:null,dataUpdateCount:1,dataUpdatedAt:1784662080622,error:null,errorUpdateCount:0,errorUpdatedAt:0,fetchFailureCount:0,fetchFailureReason:null,fetchMeta:null,isInvalidated:!1,status:"success",fetchStatus:"idle"},queryKey:$R[793]=["impersonationState"],queryHash:"[\"impersonationState\"]"},$R[794]={dehydratedAt:1784662080682,state:$R[795]={data:$R[21],dataUpdateCount:1,dataUpdatedAt:1784662080681,error:null,errorUpdateCount:0,errorUpdatedAt:0,fetchFailureCount:0,fetchFailureReason:null,fetchMeta:null,isInvalidated:!1,status:"success",fetchStatus:"idle"},queryKey:$R[796]=["sequence","156svv1"],queryHash:"[\"sequence\",\"156svv1\"]"}]}}})($R["tsr"]);document.currentScript.remove()