It requires replacing outliers in the sample with the sample mean before resampling.

It substitutes the unknown population distribution with the empirical distribution of the observed sample.

It assumes the underlying population distribution follows a known parametric form, like the normal distribution.

It directly calculates the exact standard error of an estimator using an analytical formula.

By splitting the original sample into N unique subsets.

By sampling N items from the original sample with replacement.

By fitting a normal distribution to the sample and drawing N new data points.

By sampling N items from the original sample without replacement.

The standard error of the original sample mean, calculated as $s / \sqrt{n}$.

The mean of the bootstrap distribution of the statistic.

The standard deviation of the bootstrap distribution of the statistic.

The standard deviation of the original sample.

It significantly improves computational performance by replacing slow Python loops with optimized, low-level code.

It automatically handles missing data within the original sample.

It guarantees that the bootstrap confidence intervals will be more accurate.

It allows the use of more complex statistical estimators that are not available in standard Python.

The 5th and 95th percentiles.

The minimum and maximum values.

The 2.5th and 97.5th percentiles.

The mean plus or minus 1.96 times the bootstrap standard error.

When the original sample size is very large.

When using a very high number of bootstrap replications (e.g., B > 10,000).

When the sampling distribution of the estimator is highly skewed.

When the statistic being estimated is the sample mean.

Dependent data points and non-representative samples.

Outliers in the data and a small sample size.

Computational cost and memory usage.

Bias in the bootstrap distribution and skewness in the sampling distribution.

To generate the main bootstrap replications.

To determine the optimal number of bootstrap samples (B) to run.

To estimate the 'acceleration' parameter (a), which accounts for skewness.

To directly calculate the endpoints of the confidence interval.

OOB error is calculated on the data points that were included in a specific bootstrap sample.

It is an alternative name for the cross-validation error.

It uses the observations not selected in a bootstrap sample as a validation set to estimate model prediction error.

It is a method for calculating feature importance, not model error.

The model's true AUC on new, unseen data is guaranteed to be between 0.82 and 0.88.

95% of the model's predictions will be correct.

If we were to repeat the entire data collection and modeling process many times, 95% of the calculated confidence intervals would contain the true AUC.

There is a 95% probability that the true AUC is between 0.82 and 0.88.

It is impossible to calculate a mean or median for time-series data.

Time-series data usually has too many data points, making bootstrapping computationally infeasible.

Time-series data is always normally distributed, so analytical methods are superior.

Standard resampling with replacement breaks the temporal dependence (autocorrelation) structure in the data.

It first transforms the data to remove any dependence, then applies standard bootstrapping.

It only samples every k-th data point to create a bootstrap sample.

It divides the data into contiguous blocks and resamples these blocks with replacement.

It fits a parametric model (like ARIMA) and resamples the residuals from that model.

The statistic of interest must be the mean or the median.

The original sample must be representative of the underlying population.

The original sample must be very large (N > 1000).

The underlying population distribution must be symmetric.

True

False