A Gentle Introduction to Statistical Power and Power Analysis in Python
Last Updated on April 24, 2020
The statistical power of a hypothesis test is the probability of detecting an effect, if there is a true effect present to detect.
Power can be calculated and reported for a completed experiment to comment on the confidence one might have in the conclusions drawn from the results of the study. It can also be used as a tool to estimate the number of observations or sample size required in order to detect an effect in an experiment.
In this tutorial, you will discover the importance of the statistical power of a hypothesis test and now to calculate power analyses and power curves as part of experimental design.
After completing this tutorial, you will know:
- Statistical power is the probability of a hypothesis test of finding an effect if there is an effect to be found.
- A power analysis can be used to estimate the minimum sample size required for an experiment, given a desired significance level, effect size, and statistical power.
- How to calculate and plot power analysis for the Student’s t test in Python in order to effectively design an experiment.
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