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(Social) scientists don't necessarily know statistics even though they have had some courses. It's very common e.g. not to understand what p-value actually mean
by jampekka 3y ago
(Social) scientists don't necessarily know statistics even though they have had some courses. It's very common e.g. not to understand what p-value actually means. It's disturbingly common that non-linearities or heteroscedasticity are totally ignored in regression. Assumptions of statistical tests are typically not understood.
It's largely a cargo cult. For example p-values are reported as t(N) = x, p < threshold but very few understand why and keep on doing it and even demanding it from others.
(The why is because the p-values had to be read from tables before computers. It makes no sense nowadays.)
- begemotz 3y agothis is not the only reason 'why'. Another persistent reason is the function of the alpha value in typical null hypothesis testing leading to the (misguided) idea that a p value of .04 is functionally equivalent to a p value of 2.3e-10 since both are below threshold. So i would argue that this is more damning - essentially a misunderstanding of probability and what p values tell us at a fundamental level.
- jampekka 3y agoTypically you see p < 0.01 etc even with 0.05 alpha. A lot of stats software gives only those inexact values. But yes, the interpretation of p-values and confidence levels are wildly misunderstood. p > alpha is often taken as "evidence of absence" of an effect, which is just wrong. Or when for some quantity p1 < alpha and other p2 > alpha, it's often intepreted that the quantities differ. It's a mess.