Power Analysis

With a power analysis you can determine the sample size (number of participants) you need in your experimental study to detect an effect of a certain size. Underpowered studies often lead to reproducibility problems. A power analysis can prevent this from the outset by determining your statistical power. There are four pillars that you should keep in mind when performing such an analysis: 1) effect size, 2) sample size, 3) significance and 4) statistical power. For a more detailed explanation of the concept of power analysis, please see here.

More about:

Tools:

  • G*Power (a tool to compute power analyses for many different statistical tests)
  • R package pwr (statistical power analysis in R)
  • R package BFDA (for Bayesian sample size planning)