Power is indeed irrelevant in interpreting completed studies

Editor—I agree with Bacchetti (and most correspondents).¹ Power is of no relevance in interpreting a completed study. In his classic, Planning of Experiments, Sir David Cox says that power is important in choosing between alternative methods of analysing data and in deciding on an appropriate size of experiment and that it is quite irrelevant in the analysis of data.²

I also agree with much of what Horrobin says, but he overstates the case against sample size determinations in pilot studies.¹ In most indications the variability is a function of the disease not the treatment, and the fact that the treatment has not been studied is no bar to using an estimate. The difference you are seeking is not the same as the difference you expect to find, and again you do not have to know what the treatment will do to find a figure. This is common to all science. An astronomer does not know the magnitude of new stars until he has found them, but the magnitude of star he is looking for determines how much he has to spend on a telescope.

The definition of a medical statistician is one who will not accept that Columbus discovered America because he said he was looking for India in the trial plan.³ Columbus made an error in his power calculation—he relied on an estimate of the size of the Earth that was too small, but he made one none the less, and it turned out to have very fruitful consequences.