The issues that arise when one uses statistical hypothesis testing framework with large samples are dissussed on CV (see e.g. this thread). The main problem discussed there is that in the real world the null hypothesis like "there is no connection between variable X and variable Y" is almost always false (at least in domains like social studies when the research is not based on perfect controllable randomized experiment) and so it will be rejected if our sample is large enough.
I'm looking for references to empirical studies that support "null hypothesis is almost always false" statement.
I believe there is a study where some large survey with a hundreds of questions was used. For every question the authors tested the hypothesis that the answer to that question depends on the gender. They used t-test and choose the alternative hypothesis at random. As a result, they get about 45% of significant results. I read this paper some time ago but lost the reference and cannot find it now.
Are there any other studies like this? I'm looking for them to use as an illustration in statistical courses (when I teach others), to emphasis the distinctions between ”statistical significance” and ”practical significance”.