A Gentle Introduction to Effect Size Measures in Python
Last Updated on August 8, 2019
Statistical hypothesis tests report on the likelihood of the observed results given an assumption, such as no association between variables or no difference between groups.
Hypothesis tests do not comment on the size of the effect if the association or difference is statistically significant. This highlights the need for standard ways of calculating and reporting a result.
Effect size methods refer to a suite of statistical tools from the the field of estimation statistics for quantifying an the size of an effect in the results of experiments that can be used to complement the results from statistical hypothesis tests.
In this tutorial, you will discover effect size and effect size measures for quantifying the magnitude of a result.
After completing this tutorial, you will know:
- The importance of calculating and reporting effect size in the results of experiments.
- Effect size measures for quantifying the association between variables, such as Pearson’s correlation coefficient.
- Effect size measures for quantifying the difference between groups, such as Cohen’s d measure.
Kick-start your project with my new book Statistics for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.