![]() The point here is not to specify the effect size that you expect to find or that others have found, but the smallest effect size of scientific interest. Specify the smallest effect size that is of scientific interest. Specify the significance level of the test. Make them explicit in terms of a null and alternative hypothesis. Most studies have many hypotheses, but for sample size calculations, choose one to three main hypotheses. But regardless of which way you or your statistician calculates it, you need to first do the following 5 steps: Once you’ve gathered that information, you can calculate by hand using a formula found in many textbooks, use one of many specialized software packages, or hand it over to a statistician, depending on the complexity of the analysis. But first you need to gather some information about on which to base the estimates. If your effect turns out to be bigger, so much the better. The trick is to size a study so that it is just large enough to detect an effect of scientific importance. Both expose an unnecessary number of participants to experimental risks. Both undersized and oversized studies waste time, energy, and money the former by using resources without finding results, and the latter by using more resources than necessary. Why? Undersized studies can’t find real results, and oversized studies find even insubstantial ones. But all studies are well served by estimates of sample size, as it can save a great deal on resources. ![]() Nearly all granting agencies require an estimate of an adequate sample size to detect the effects hypothesized in the study. ![]()
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