 Meta-analytic problems, such as pooling various studies for determining the overall effect size of an intervention or risk factor give a particularly interesting twist to Simpson’s Paradox. How should such studies be aggregated? Naïvely, somebody may suggest to pool the data from all studies and to treat them as a single big study. This may work out if the study populations are very similar and the data are from RCTs, where the treatment/control ratio is typically 50:50. If this is indeed the case, then the overall dataset is row-uniform and AR (and for most measures, AMP) is avoided, as shown in  Section 2.2.  But for non-experimental data, there is no reason to assume that treatment/control proportions are equal across studies. Thus, the direction of the direction of the effect can be reversed when pooling (for examples, see Hanley &amp; Thériault 2000; Reintjes, Boer, Pelt, &amp; Mintjes-de Groot 2000; Rücker &amp; Schumacher 2008).