What to do with "null" results - Part II: Nonsignificant but with sufficient data
What to do with "null" results Part II: Nonsignificant but with sufficient data PREAMBLE I assume you have some familiarity with frequentist stats and NHST I will default to general conventions to avoid unnecessary verbiage (e.g., "p > .05" instead of saying "a p-value higher than your per-selected alpha criterion for long-run Type I error control") When possible, I will explain a concept briefly instead of just pointing to a 300+ page statistics book, but with the implied risk my explanation will be limited. this is a guide (i explain something) not a tutorial (i show you how to do it); I can do a tutorial if people want (let me know in the comments) I will only use open license software to explain concepts, but no R code unless I have to; I know people might just want to implement solutions with things like JASP and G*Power directly. PREMISE I continue the series of what to do with nonsignificant (aka. "null") results. In this p