How to do bootstrap test

Bootstrap inspection

What is Bootstrap inspection?

The Bootstrap test is a non-parametric test method used to evaluate whether differences in sample statistics are statistically significant. It estimates the sampling distribution of a statistic by repeatedly sampling from the original data set and calculating the statistic for each sample.

Bootstrap test steps

1. Repeated sampling from the original data set: Randomly sample from the original data set through sampling with replacement Take multiple samples.
2. Calculate statistics for each sample: For each sample drawn, calculate the statistic of interest, such as the mean, median, or difference.
3. Create the sampling distribution of statistics: Collect all statistics calculated by repeated sampling and create their distribution histograms.
4. Compute the p-value of the original statistic: Compare the calculated statistic from the original data set to the sampling distribution. The p-value is the probability that the original statistic falls at the extreme end of the sampling distribution.
5. Conclusion: If the p-value is less than a preset significance level (usually 0.05), the null hypothesis is rejected, that is, the difference in the sample statistics is statistically significant.

Advantages of Bootstrap test

• No need to make assumptions about data distribution
• More reliable for small sample data
• Can be used to evaluate a variety of statistics

Disadvantages of the Bootstrap test

• May be computationally intensive, especially for large data sets
• May be less accurate for data that is highly skewed or has outliers
• Cannot be used to evaluate parameters such as variance or standard deviation

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