In my last post, I argued that DDS type calculations (also called Neymanian power analysis) provide needful information to avoid fallacies of acceptance in the test T+; whereas, the corresponding confidence interval does not (at least not without special testing supplements). But some have argued that DDS computations are “fundamentally flawed” leading to what is called the “power approach paradox”, e.g., Hoenig and Heisey (2001).
We are to consider two variations on the one-tailed test T+: H0: μ ≤ 0 versus H1: μ > 0 (p. 21). Following their terminology and symbols: The Z value in the first, Zp1, exceeds the Z value in the second, Zp2, although the same observed effect size occurs in both[i], and both have the same sample size, implying that σ1 < σ2. For example, suppose σx1 = 1 and σx2 = 2. Let observed sample mean M be 1.4 for both cases, so Zp1 = 1.4 and Zp2 = .7. They note that for any chosen power, the computable detectable discrepancy size will be smaller in the first experiment, and for any conjectured effect size, the computed power will always be higher in the first experiment.
“These results lead to the nonsensical conclusion that the first experiment provides the stronger evidence for the null hypothesis (because the apparent power is higher but significant results were not obtained), in direct contradiction to the standard interpretation of the experimental results (p-values).” (p. 21)
But rather than show the DDS assessment “nonsensical”, nor any direct contradiction to interpreting p values, this just demonstrates something nonsensical in their interpretation of the two p-value results from tests with different variances. Since it’s Sunday night and I’m nursing[ii] overexposure to rowing in the Queen’s Jubilee boats in the rain and wind, how about you find the howler in their treatment. (Also please inform us of articles pointing this out in the last decade, if you know of any.)
Hoenig, J. M. and D. M. Heisey (2001), “The Abuse of Power: The Pervasive Fallacy of Power Calculations in Data Analysis,” The American Statistician, 55: 19-24.