“Power” to detect statistically significant effects based on sample size and magnitudes of effects

“Power” to detect statistically significant effects based on sample size and magnitudes of effects

I was going through magnitude-based inferences materials by Will Hopkins and I am playing with R simulations. I wanted to see how many times I am able to detect statisticaly significant effects (p<0.05) depending on magnitude of effects (expressed as Cohen’s D, and using Will Hopkins levels) and sample sizes.

What I did is created a baseline group (mean = 100, SD = 10), and 5 more groups based on magnitude of difference (Trivial, Small, Medium, Large, Very Large) and repeated this for different number of subjects. Then I calculated p values using t test between baseline group and 5 other groups for each number of subjects. Then I repeated this process 1000 times and counder significant effects (p<0.05).

The result is the table showing how many times (percentage) in those 1000 resampling I was able to detect statisticly significant effect depending on the number of subjest of magnitude of change (from baseline group).

Here is the code and the resulting table:

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I am a physical preparation coach from Belgrade, Serbia, grew up in Pula, Croatia (which I consider my home town). I was involved in physical preparation of professional, amateur and recreational athletes of various ages in sports such as basketball, soccer, volleyball, martial arts and tennis. Read More »
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