How can you use resampling techniques to improve hypothesis testing?

1 What are resampling techniques?

Resampling techniques are methods of creating new data samples from an existing data set by using random selection or manipulation. The idea is to mimic the process of sampling from the population of interest, without making any assumptions about its distribution or parameters. Resampling techniques can be used for various purposes, such as estimating confidence intervals, testing hypotheses, comparing groups, or assessing model performance.

3 When to use resampling techniques?

Resampling techniques can be beneficial when dealing with certain challenges, such as a small sample size that does not permit the use of conventional methods, a non-normal or skewed data distribution that violates the assumptions of parametric methods, a complex data structure that involves dependencies, heterogeneity, or outliers, or a novel or custom statistic or function without a known sampling distribution or analytical solution.

4 What are some examples of resampling techniques?

Resampling techniques are commonly used to estimate confidence intervals, standard errors, or bias corrections for any statistic or function. Bootstrap, permutation, jackknife and cross-validation are all examples of resampling techniques. Bootstrap involves drawing random samples with replacement from the original data set. Permutation involves shuffling or rearranging the values of the original data set to test hypotheses or compare groups. Jackknife involves leaving out one observation at a time from the original data set and recalculating the statistic or function. Cross-validation splits the original data set into training and testing subsets and evaluates the performance of a model or a method on each subset. Resampling techniques can be used to assess accuracy, precision, or generalizability of a model, as well as to optimize its parameters or features and detect outliers or perform robust estimation.

5 How can you implement resampling techniques in Python?

Python offers several libraries and modules to facilitate resampling techniques. For instance, Scipy provides functions for bootstrap, permutation, and jackknife methods, such as the scipy.stats.bootstrap function for bootstrap resampling and confidence interval estimation. Sklearn is another library that has tools for machine learning and data analysis, including cross-validation methods like k-fold, leave-one-out, or stratified splits. Additionally, Resample is a library that has a straightforward interface to resample data in pandas data frames; the resample.bootstrap function allows you to perform bootstrap resampling and return a new data frame with the resampled values.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?