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power analysis
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How many participants do you need when comparing several treatment arms?
Plan a multi-arm study around the comparisons you will report, not the ANOVA F test, and see how a multiplicity correction changes the numbers.
Read the answer →False positive risk: how likely is a significant finding to be wrong?
Why low power raises the share of significant findings that are false, inflates the real effects that get through, and what one researcher can do.
Read the answer →How are alpha and beta (Type I and Type II error rates) related?
Why lowering alpha raises beta, why the trade-off is not linear or a fixed ratio, and what a Bonferroni correction does to power.
Read the answer →Is a non-significant result from a large study evidence for the null?
When failing to reject the null hypothesis tells you something, why the p value alone cannot show it, and how equivalence tests make the case.
Read the answer →Does a t test need a minimum sample size?
Why no fixed minimum n makes a t test valid, what small samples really cost (power and robustness), and how to justify your sample size.
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