Were “outliers” analysed with both common sense and appropriate statistical adjustments?
Unexpected results may reflect idiosyncrasies in the subject (for example, unusual metabolism), errors in measurement (faulty equipment), errors in interpretation (misreading a meter reading), or errors in calculation (misplaced decimal points). Only the first of these is a “real” result which deserves to be included in the analysis. A result which is many orders of magnitude away from the others is less likely to be genuine, but it may be so. A few years ago, while doing a research project, I measured several different hormones in about 30 subjects. One subject’s growth hormone levels came back about 100 times higher than everyone else’s. I assumed this was a transcription error, so I moved the decimal point two places to the left. Some weeks later, I met the technician who had analysed the specimens and he asked, “Whatever happened to that chap with acromegaly?” Statistically correcting for outliers (for example, to modify their effect on the overall result) requires sophisticated an
