ABSTRACT
It is well known that, when a null hypothesis has been rejected, it simply means that the assertion made in the null hypothesis (H 0) is unlikely to be true in the population under study. When that assertion is that the independent variables has no effect on the dependent variable, then the rejection of H 0 tells us only that the effect is not likely to be zero in the relevant population. It does not suggest that the effect is large or even nontrivial but simply that it is nonzero. Hence, the rejection of H 0 “cries out” for estimating the magnitude of the effect in question. This, combined with another well-known fact that the larger our sample size is, the easier it is (other things being equal) to reject a null hypothesis, makes it reasonable to give a general, conceptual definition of effect size (as Rosenthal does in chap. 20) in the following way: https://www.w3.org/1998/Math/MathML"> Effect size = [ significance-test statistic ] / [ sample size ] https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781315799582/b70f8155-196f-45a0-84ff-2b7a440bb1b8/content/math_131_B.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/>
