Against NHST


57


gwern

A summary of standard non-Bayesian criticisms of common frequentist statistical practices, with pointers into the academic literature.

Frequentist statistics is a wide field, but in practice by innumerable psychologists, biologists, economists etc, frequentism tends to be a particular style called “Null Hypothesis Significance Testing” (NHST) descended from R.A. Fisher (as opposed to eg. Neyman-Pearson) which is focused on

  1. setting up a null hypothesis and an alternative hypothesis
  2. calculating a p-value (possibly via a _<_a href="https://en.wikipedia.org/wiki/Student%27s_t-test">t-test or more complex alternatives like ANOVA)
  3. and rejecting the null if an arbitrary threshold is passed.

NHST became nearly universal between the 1940s & 1960s (see Gigerenzer 2004, pg18), and has been heavily criticized for as long. Frequentists criticize it for:

  1. practitioners & statistics teachers misinterpret the meaning of a p-value (LessWrongers too); Cohen on this persistent illusion:

What’s wrong with NHST? Well, among other things, it does not tell us what we want to know, and we so much want to know what we want to know that, out of desperation, we nevertheless believe that it does! What we want to know is, “Given these data, what is the probability that H0 is true?” But as most of us know, what it tells us is “Given that H0 is true, what is the probability of these (or more extreme) data?” These are not the same…

(This misunderstanding is incredibly widespread; once you understand it, you'll see it everywhere. I can't count how many times I have seen a comment or blog explaining that a p=0.05 means "the probability of the null hypothesis not being true is 95%", in many different variants.)

  1. cargo-culting the use of 0.05 as an accept/reject threshold based on historical accident & custom (rather than using a loss function chosen through decision theory to set the threshold based on the cost of false positives).

Similarly, the cargo-culting encourages misuse of two-tailed tests, avoidance of multiple correction, data dredging, and in general, “p-value hacking”.

  1. failing to compare many possible hypotheses or models, and limiting themselves to one - sometimes ill-chosen or absurd - null hypothesis and one alternative
  2. deprecating the value of exploratory data analysis and depicting data graphically (see, for example, Anscombe’s quartet)
  3. ignoring the more important summary statistic of “effect size”
  4. ignoring the more important summary statistic of confidence intervals; this is related to how use of p-values leads to ignorance of the statistical power of a study - a small study may have only a small chance of detecting an effect if it exists, but turn in misleadingly good-looking p-values
  5. because null hypothesis tests cannot accept the alternative, but only reject a null, they inevitably cause false alarms upon repeated testing

(An example from my personal experience of the cost of ignoring effect size and confidence intervals: p-values cannot (easily) be used to compile a meta-analysis (pooling of multiple studies); hence, studies often do not include the necessary information about means, standard deviations, or effect sizes & confidence intervals which one could use directly. So authors must be contacted, and they may refuse to provide the information or they may no longer be available; both have happened to me in trying to do my dual n-back & iodine meta-analyses.)

Critics’ explanations for why a flawed paradigm is still so popular focus on the ease of use and its weakness; from Gigerenzer 2004:

Hays (1963) had a chapter on Bayesian statistics in the second edition of his widely read textbook but dropped it in the subsequent editions. As he explained to one of us (GG) he dropped the chapter upon pressure from his publisher to produce a statistical cookbook that did not hint at the existence of alternative tools for statistical inference. Furthermore, he believed that many researchers are not interested in statistical thinking in the first place but solely in getting their papers published (Gigerenzer, 2000)…When Loftus (1993) became the editor of Memory & Cognition, he made it clear in his editorial that he did not want authors to submit papers in which p-, t-, or F-values are mindlessly being calculated and reported. Rather, he asked researchers to keep it simple and report figures with error bars, following the proverb that “a picture is worth more than a thousand p-values.” We admire Loftus for having had the courage to take this step. Years after, one of us (GG) asked Loftus about the success of his crusade against thoughtless significance testing. Loftus bitterly complained that most researchers actually refused the opportunity to escape the ritual. Even when he asked in his editorial letter to get rid of dozens of p-values, the authors insisted on keeping them in. There is something deeply engrained in the minds of many researchers that makes them repeat the same action over and over again.

Shifts away from NHST have happened in some fields. Medical testing seems to have made such a shift (I suspect due to the rise of meta-analysis):

Fidler et al. (2004b, 626) explain the spread of the reform in part by a shift from testing to estimation that was facilitated by the medical literature, unlike psychology, using a common measurement scale, to “strictly enforced editorial policy, virtually simultaneous reforms in a number of leading journals, and the timely re-writing [of] textbooks to fit with policy recommendations.” But their description of the process suggests that an accidental factor, the coincidence of several strong-willed editors, also mattered. For the classic collection of papers criticizing significance tests in psychology see Morrison and Hankel (1970) [The Significance Test Controversy: A Reader], and for a more recent collection of papers see Harlow et al. (1997) [What If There Were No Significance Tests?]. Nickerson (2000) provides a comprehensive survey of this literature.

0.1 Further reading

More on these topics:

The perils of NHST, and the merits of Bayesian data analysis, have been expounded with increasing force in recent years (e.g., W. Edwards, Lindman, & Savage, 1963; Kruschke, 2010b, 2010a, 2011c; Lee & Wagenmakers, 2005; Wagenmakers, 2007).

Although the primary emphasis in psychology is to publish results on the basis of NHST (Cumming et al., 2007; Rosenthal, 1979), the use of NHST has long been controversial. Numerous researchers have argued that reliance on NHST is counterproductive, due in large part because p values fail to convey such useful information as effect size and likelihood of replication (Clark, 1963; Cumming, 2008; Killeen, 2005; Kline, 2009 [Becoming a behavioral science researcher: A guide to producing research that matters]; Rozeboom, 1960). Indeed, some have argued that NHST has severely impeded scientific progress (Cohen, 1994; Schmidt, 1996) and has confused interpretations of clinical trials (Cicchetti et al., 2011; Ocana & Tannock, 2011). Some researchers have stated that it is important to use multiple, converging tests alongside NHST, including effect sizes and confidence intervals (Hubbard & Lindsay, 2008; Schmidt, 1996). Others still have called for NHST to be completely abandoned (e.g., Carver, 1978).

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