Physical laws, Scientific statistics, Engineering allowances, and Psychological validity

Science seemed simple when I started my undergraduate studies in chemistry 50 years ago. Organic chemistry was not easy, but once you knew the rules (i.e., got the stochiometry right), 2 + 2 always = 4. With no intrinsic variability, the only sources of deviation were instrument or reader error. Moving on to graduate studies in physiology, things got more complicated. However, some 15 years earlier Ronald Fisher in England had provided powerful statistical tools based on null hypothesis testing. These methods proved whether differences between two groups were significant. Of course the usually accepted 5% significance level (i.e., p < 0.05) still meant there was one chance in 20 that the difference was sheer coincidence. More rigorous (or luckier) researchers were using p < . 01 (one chance in 100). Optimistic researchers sometimes submitted research papers citing p ~ 0.07 or used such euphemisms as generally showed good agreement, but these seldom raised the editorial bar to publication in most scientific journals. I was one of the luckier ones, for a while. My subjects were highly inbred white rats, living isolated in individual cages in essentially identical, invariable thermal conditions of 12 hours of light/12 hours of darkness, fed a constant lab chow diet, etc. And the results of my research on adrenal gland regeneration and function in response to stress following surgical removal, exposure to identical doses of X-irradiation, and re-implantation proved to be normally distributed (i.e., followed the classic bell shaped curve). My research at Boston University (post Hiroshima) was deemed sufficiently important to be supported by the Atomic Energy Commission, which allowed my sample size (n) to be increased to 30 to 50 subjects in any sub-study where the results were not totally obvious. There was no need to run regression analyses with their correlation coefficients (r values) and the very informative coefficient of determination (r2), which defined what percent of the variability in data was explained by the hypothesis being tested; i.e., even though one might find a statistically significant (p < 0.05) r value of 0.4, this meant the hypothesis being tested only explained 16% of the response. Becoming allergic to animals, I moved to a U.S. Army Laboratory at Natick, Mass. , to study the effects of heat, cold, and work stresses on a soldier’s mission performance. The subjects, enlisted military, volunteered for a given study only after being fully informed as to the degree of discomfort (usually high), of risk (usually low), and of their right (absolute and without prejudice) to withdraw from any exposure at any time. In essence, they traded a few hours a day of discomfort during a 5-day workweek for the generic discomfort of a 7-day week of military duty. Unfortunately for science, their environmental and genetic backgrounds varied wildly, including an ex-street thug given the choice of military service or jail and a multi-millionaire’s son interested in science/medicine who was happy to have weekends free near such all-girls schools as Wellesley, Radcliff, and Smith College. I learned that the usual requirement of 6 subjects for a 5% significance level was inadequate for human subjects studied under less than severe stress conditions. Whether one accepts the 1779 dogma of Archbishop Ussher that man was created at 9 a.m. on Oct. 23rd, 4004 BC, at Eden on the Euphrates, or the archaeological-anthropological dating of modern man to ~ 45,000 BC, man is a new species with large inter-individual variability. Human variation is so large that it proved necessary to study the