This tutorial demonstrates how simulations can enhance understanding of sampling distributions and statistical inferences in research, highlighting potential pitfalls.
Key Points
Simulations reveal the uncertainty in experimental estimations, stressing caution in interpreting single-experiment results.
Frequentist statistics often overlook sampling distributions, making explicit understanding critical for accurate inferences.
The tutorial showcases graphically descriptive examples using correlation analyses and illustrates the consequences of arbitrary effect size cut-offs.
Understanding the data-generating process through simulations aids in reducing false positives and improving research reliability.
Cite This Study
Guillaume A. Rousselet (2025) studied this question.