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:

• When Power Analyses Based on Pilot Data are Biased: Inaccurate Effect Size Estimators and Follow-up Bias (Albers & Lakens, 2018)
• Using Anchor-Based Methods to Determine the Smallest Effect Size of Interest (Anvari & Lakens, 2019)
• Correcting for Bias in Psychology: A Comparison of Meta-Analytic Methods (Carter et al., 2019)
• Safeguard Power as a Protection Against Imprecise Power Estimates (Perugini et al., 2014)
• A tutorial on Bayes Factor Design Analysis using an informed prior (Stefan et al., 2019)

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)