Bootstrap Sampling Simulator
Overview
The Bootstrap Sampling Simulator demonstrates one of the most powerful and practical techniques in modern statistics. By resampling from your data with replacement, you can estimate confidence intervals and standard errors without assuming any particular distribution. Watch as thousands of bootstrap samples reveal the sampling distribution of statistics, providing robust inference even with small datasets.
Tips
- Start with a small sample (n=20-30) to see how bootstrap works even when you have limited data
- Generate at least 1000 bootstrap samples for stable confidence interval estimates - more is better
- Compare the bootstrap distribution to the theoretical distribution (when known) to verify the method works
- The 95% confidence interval captures the middle 95% of bootstrap estimates - the 2.5th to 97.5th percentiles
- Bootstrap works for any statistic - mean, median, standard deviation, correlation, or custom functions
- Try different sample sizes to see how larger samples produce narrower confidence intervals
- The bootstrap distribution shape reveals the sampling variability of your statistic
- Bootstrap is especially powerful when theoretical formulas are unknown or complex (like for medians or ratios)
- Remember: bootstrap assumes your sample is representative of the population