Which resampling technique estimates the sampling distribution by drawing samples with replacement from the data?

Which resampling technique estimates the sampling distribution by drawing samples with replacement from the data?

• A. Boxplot
• B. Boredom effect
• C. Box-whisker plot
• D. Bootstrap

Answer: (D)

Estimating the sampling distribution by drawing samples with replacement from the data is the bootstrap. In bootstrap, you treat your observed data as if it were the population, then repeatedly draw new samples of the same size with replacement. For each resample, you compute the statistic of interest, and the distribution of those statistics across many resamples serves as an approximation to the true sampling distribution. This lets you estimate standard errors, confidence intervals, and bias without relying on strong parametric assumptions. The other options aren’t resampling methods. A boxplot or box-whisker plot is a graphical summary of the data’s distribution (quartiles, median, and potential outliers). The term listed as the other option isn’t a recognized statistical resampling technique.

Estimating the sampling distribution by drawing samples with replacement from the data is the bootstrap. In bootstrap, you treat your observed data as if it were the population, then repeatedly draw new samples of the same size with replacement. For each resample, you compute the statistic of interest, and the distribution of those statistics across many resamples serves as an approximation to the true sampling distribution. This lets you estimate standard errors, confidence intervals, and bias without relying on strong parametric assumptions.

The other options aren’t resampling methods. A boxplot or box-whisker plot is a graphical summary of the data’s distribution (quartiles, median, and potential outliers). The term listed as the other option isn’t a recognized statistical resampling technique.