When researchers carry out a null hypothesis significance test, it is tempting to assume that a statistically significant result lowers Prob(H0), the probability of the null hypothesis being true.Technically, such a statement is meaningless for various reasons: e.g., the null hypothesis does not have a probability associated with it.
However, it is possible to relax certain assumptions to compute the posterior probability Prob(H0) Soccer - Shoes Outdoor - Junior under repeated sampling.We show in a step-by-step guide that the intuitively appealing belief, that Prob(H0) is low when significant results have been obtained under repeated sampling, is in general incorrect and depends greatly on: (a) the prior probability of the null being true; (b) type-I error rate, (c) type-II error rate, and (d) replication of a result.Through step-by-step simulations using open-source code in the R System of Statistical Computing, we show that uncertainty about the null hypothesis Canvas Prints being true often remains high despite a significant result.To help the reader develop intuitions about this common misconception, we provide a Shiny app (https://danielschad.
shinyapps.io/probnull/).We expect that this tutorial will help researchers better understand and judge results from null hypothesis significance tests.