Abstract

Suspicion of “physics envy” surrounds the standard statistical toolbox used in the empirical sciences, from biology to psychology. Mainstream methods in these fields, various lines of criticism point out, often fall short of the basic requirements of measurement. Quantitative scales are applied to variables that can hardly be treated as measurable magnitudes, like preferences or happiness; hypotheses are tested by comparing data with conventional significance thresholds that hardly mention effect sizes. This article discusses what I call “shmeasurement.” To “shmeasure” is to fail to apply quantitative tools to quantitative questions. We “shmeasure” when we try to measure what cannot be measured, or, conversely, when we ask binary questions of continuous measurements. Following the critics of standard statistical tools, it is argued that our statistical toolbox is indeed less concerned with the measurement of magnitudes than we take it to be. This article adds, however, that measurement is not all there is to scientific activity. Most techniques of proof do not resemble measurement as much as voting—a practice that makes frequent use of numbers, figures, or measurements, yet is not chiefly concerned with assessing quantities. Measurement is only one among three functions of the scientific toolbox, the other two being collating observations and deciding which hypotheses to relinquish. I thus make a plea for “shmeasurement”: the mismeasure of things starts to make more sense once we take into account the nonquantitative side of scientific practice