Attempts to detect anticipation in datasets have been hampered by two statistical problems: confusion about the role of correlation, and ascertainment bias. We show three things. (1) Correlation and anticipation are two distinct phenomena, such that neither high nor low correlation necessarily reveals anything about anticipation. (2) Nor does correlation analysis (or its cousin, regression analysis) offer a way to detect ascertainment bias, as has been suggested. Demonstration of “regression to the mean” does not necessarily indicate the presence of ascertainment bias, either. (3) Finally, under certain special circumstances, one can test for anticipation without regard for ascertainment bias. However, these circumstances are limited and may prove of little practical value.