300/(1 - 0.1)[1] 333.33337 Tips
7.1 Adjusting for Dropout
If you anticipate dropouts or non-response, expressed as a proportion \(p\), inflate the sample size as follows (Crespi 2025):
\[ \frac{N}{1 - p}\]
For example, if you estimate a sample size of 300 but anticipate a dropout rate of 0.1, plan on inflating your sample size by 34 subjects to 334.
7.2 Post-Hoc Power
Post-Hoc Power is not really a thing.
In studies that fail to yield “statistically significant” results, it is common for reviewers, or even editors, to ask the authors to include a post hoc power calculation. In such situations, editors would like to distinguish between true negatives and false negatives (concluding there is no effect, when there actually is an effect, and the study was just too small to pick it up). However, reporting post-hoc power is nothing more than reporting the p-value a different way, and will therefore not answer the question editors want to know.
7.3 Using AI
If you want to use AI to perform power and sample size calculations, request the computer code to perform the calculations instead of asking AI to do the work and provide the answer. For example,
I am planning an experiment that will compare the means of two groups using a two-sided two-sample t-test. I would like my sample to be large enough to detect a medium effect with 0.90 power at a significance level of 0.05. Provide R code to carry out this calculation.