Statistics in psychology

Andrew Gelman links to a pretty horrendous explanation of p-values. Old story. I think Gigerenzer complained a while back that few psychologists know what a p-value is (and that everyone should be looking at posterior densities, or whatever, anyway…).  Shame that the BPS is peddling this, though.

Okay, but it triggered an interesting comment from “anonymous”:

“Although I applaud any effort to help students with difficult subjects, I believe psychology has been ‘dumbed down’ well enough. I understand that from an ideological (‘yes-you-can-as-long-as-you-want-to’), as well as a financial (e.g. in the Netherlands, universities get funding as a function of number of students attracted vs number of students finishing degrees), there is a hope that every single person in the world can and should study and finish psychology. In the end, however, some people just fail to understand something incredibly simple because they lack either the ability or the motivation. In my mind, they should not be helped to study, but helped to find something else to do with their life.”

One problem is that p-values just aren’t explained properly to psychology students, so even those who are motivated and have the ability get brain damaged.  This is only one of many problems with statistics education in many psychology departments, another being the ridiculous belief many students have that “parametric” means “normally distributed”.

Wolfowitz (1942) [Additive Partition Functions and a Class of Statistical Hypotheses The Annals of Mathematical Statistics, 1942, 13, 247-279] writes:
“the distribution functions [note: plural!!!] of the various stochastic variables which enter into their problems are assumed to be of known functional form, and the theories of estimation and of testing hypotheses are theories of estimation of and of testing hypotheses about, one or more parameters, finite in number, the knowledge of which would completely determine the various distribution functions involved. We shall refer to this situation for brevity as the parametric case, and denote the opposite situation, where the functional forms of the distributions are unknown’, as the non-parametric case.
Interestingly, Noether (1984) [Nonparametrics: The Early Years-Impressions and Recollections The American Statistician, 1984, 38, 173-178], who did work on nonparametric stats, wrote:

“The term nonparametric may have some historical significance and meaning for theoretical statisticians, but it only serves to confuse applied statisticians.”

Another:  the belief that the dependent variables of a parametric test must be unconditionally normally distributed.  Why do people persist in believing this?  Partly because it’s just not explained properly.

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One comment

  1. disgruntledphd

    Andy,

    Speaking as a psychologist, you are totally right. I myself thought parametric meant normally distributed until the end of my undergraduate programme. I also fell for the p value thing.

    That being said, bayesian statistics and my currently ongoing phd have been kind, and I no longer think either of these things to be true. I didn’t find out this was the case from psychology lecturers or books though, rather from reading stats textbooks.

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