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Power for experimental design

Imagine that you collected a sample of 100 people for your study and spend time and money executing it. After your study was done, you realized that the power of your test was only 10%. In other words, even if there was a difference between your groups, there is only a 10% chance your test would detect it, given the data you supplied. What a waste of effort!

Therefore, the best practice is to estimate power before collecting data and running an experiment. In this exercise you'll organize the steps followed in this process. The items below are steps in the process of experimental design using power analysis.

This exercise is part of the course

Foundations of Inference in Python

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