Bootstrap Difference of Means Calculator
Explore the power of bootstrapping to estimate the difference of means between two samples. Enter your data, adjust the number of resamples, and visualize the results.
Enter Your Data
Input your two sample datasets as comma-separated values.
Higher numbers of resamples provide more stable estimates but increase calculation time.
Results
Bootstrapped Mean Difference:
95% Confidence Interval:
Distribution of Bootstrapped Mean Differences
Understanding Bootstrap Difference of Means
The Bootstrap Difference of Means method is a statistical technique used to estimate the sampling distribution of the difference between the means of two independent groups. It's particularly useful when the assumptions of traditional parametric tests are not met, or when you want to understand the variability of your statistic.
How it works: We repeatedly resample with replacement from each of the original samples to create many "bootstrap samples". For each set of bootstrap samples, we calculate the difference in means. The distribution of these bootstrapped mean differences approximates the sampling distribution of the difference of means.
Confidence Interval: The 95% confidence interval provided is estimated from the central 95% of the bootstrapped distribution, giving a range within which we can be 95% confident the true difference of means lies.
This tool helps visualize and quantify the uncertainty in the difference of means, offering a robust approach to statistical inference.