So, without getting into the reasons, I’m reading through the entry in the International Encyclopedia of Statistical Science on “Statistical Fallacies: Misconceptions and Myths”, written by one “Shlomo Sawilowsky, Professor, Wayne State University, Detroit MI, USA”. Within the entry, 20 such fallacies are each briefly described.
Sawilowsky introduces the topic by stating:
Compilations and illustrations of statistical fallacies, misconceptions, and myths abound…The statistical faux pas is appealing, intuitive, logical, and persuasive, but demonstrably false. They are uniformly presented based on authority and supported based on assertion…these errors spontaneously regenerate every few years, propagating in peer reviewed journal articles…and dissident literature. Some of the most egregious and grievous are noted below.
Great, let’s get after it then.
He then gets into his list, which proceeds through a set of +/- standard types of issues, including misunderstanding of the Central Limit Theorem, Type I errors, p values, effect sizes and etc. Up comes item 14:
(a) We live in a Chi-square society due to political correctness that dictates equality of outcome instead of equality of opportunity. The test of independence version of this statistic is accepted sans voire dire by many legal systems as the single most important arbiter of truth, justice, and salvation. It has been asserted that any statistical difference between (often even nonrandomly selected) samples of ethnicity, gender, or other demographic as compared with (often even inaccurate, incomplete, and outdated) census data is primae faciea evidence of institutional racism, sexism, or other ism. A plaintiff allegation that is supportable by a significant Chi-square is often accepted by the court (judges and juries) praesumptio iuris et de iure. Similarly, the goodness of fit version of this statistic is also placed on an unwarranted pedestal.
Now this is exactly what I want from my encyclopedia entries: a strictly apolitical, logical description of the issue at hand. In fact, I hope to delve deep into other statistical writings of Dr. Sawilowsky to gain, hopefully, even better insights than this one.
Postscript: I’m not really bent out of shape on this, and would indeed read his works (especially this one: Sawilowsky, S. (2003) Deconstructing arguments from the case against hypothesis testing. J. Mod. Appl. Stat. Meth. 2(2):467-474). I can readily overlook ideologically driven examples like this to get at the substance I’m after, but I do wonder how a professional statistician worked that into an encyclopedia entry.
I note also that the supposed “screening fallacy” popular on certain blogs is not included in the list…and I’m not the least bit surprised.