replication crisis

Why hasn’t the ASA Board revealed the recommendations of its new task force on statistical significance and replicability?

something’s not revealed

A little over a year ago, the board of the American Statistical Association (ASA) appointed a new Task Force on Statistical Significance and Replicability (under then president, Karen Kafadar), to provide it with recommendations. [Its members are here (i).] You might remember my blogpost at the time, “Les Stats C’est Moi”. The Task Force worked quickly, despite the pandemic, giving its recommendations to the ASA Board early, in time for the Joint Statistical Meetings at the end of July 2020. But the ASA hasn’t revealed the Task Force’s recommendations, and I just learned yesterday that it has no plans to do so*. A panel session I was in at the JSM, (P-values and ‘Statistical Significance’: Deconstructing the Arguments), grew out of this episode, and papers from the proceedings are now out. The introduction to my contribution gives you the background to my question, while revealing one of the recommendations (I only know of 2). 

[i] Linda Young, (Co-Chair), Xuming He, (Co-Chair) Yoav Benjamini, Dick De Veaux, Bradley Efron, Scott Evans, Mark Glickman, Barry Graubard, Xiao-Li Meng, Vijay Nair, Nancy Reid, Stephen Stigler, Stephen Vardeman, Chris Wikle, Tommy Wright, Karen Kafadar, Ex-officio. (Kafadar 2020)

You can access the full paper here.

 

Rejecting Statistical Significance Tests: Defanging the Arguments^

Abstract: I critically analyze three groups of arguments for rejecting statistical significance tests (don’t say ‘significance’, don’t use P-value thresholds), as espoused in the 2019 Editorial of The American Statistician (Wasserstein, Schirm and Lazar 2019). The strongest argument supposes that banning P-value thresholds would diminish P-hacking and data dredging. I argue that it is the opposite. In a world without thresholds, it would be harder to hold accountable those who fail to meet a predesignated threshold by dint of data dredging. Forgoing predesignated thresholds obstructs error control. If an account cannot say about any outcomes that they will not count as evidence for a claim—if all thresholds are abandoned—then there is no a test of that claim. Giving up on tests means forgoing statistical falsification. The second group of arguments constitutes a series of strawperson fallacies in which statistical significance tests are too readily identified with classic abuses of tests. The logical principle of charity is violated. The third group rests on implicit arguments. The first in this group presupposes, without argument, a different philosophy of statistics from the one underlying statistical significance tests; the second group—appeals to popularity and fear—only exacerbate the ‘perverse’ incentives underlying today’s replication crisis. 

1. Introduction and Background 

Today’s crisis of replication gives a new urgency to critically appraising proposed statistical reforms intended to ameliorate the situation. Many are welcome, such as preregistration, testing by replication, and encouraging a move away from cookbook uses of statistical methods. Others are radical and might inadvertently obstruct practices known to improve on replication. The problem is one of evidence policy, that is, it concerns policies regarding evidence and inference. Problems of evidence policy call for a mix of statistical and philosophical considerations, and while I am not a statistician but a philosopher of science, logic, and statistics, I hope to add some useful reflections on the problem that confronts us today. 

In 2016 the American Statistical Association (ASA) issued a statement on P-values, intended to highlight classic misinterpretations and abuses. 

The statistical community has been deeply concerned about issues of reproducibility and replicability of scientific conclusions. …. much confusion and even doubt about the validity of science is arising. (Wasserstein and Lazar 2016, p. 129) 

The statement itself grew out of meetings and discussions with over two dozen others, and was specifically approved by the ASA board. The six principles it offers are largely rehearsals of fallacious interpretations to avoid. In a nutshell: P-values are not direct measures of posterior probabilities, population effect sizes, or substantive importance, and can be invalidated by biasing selection effects (e.g., cherry picking, P-hacking, multiple testing). The one positive principle is the first: “P-values can indicate how incompatible the data are with a specified statistical model” (ibid., p. 131). 

The authors of the editorial that introduces the 2016 ASA Statement, Wasserstein and Lazar, assure us that “Nothing in the ASA statement is new” (p. 130). It is merely a “statement clarifying several widely agreed upon principles underlying the proper use and interpretation of the p-value” ( p. 131). Thus, it came as a surprise, at least to this outsider’s ears, to hear the authors of the 2016 Statement, along with a third co-author (Schirm), declare in March 2019 that: “The ASA Statement on P-Values and Statistical Significance stopped just short of recommending that declarations of ‘statistical significance’ be abandoned” (Wasserstein, Schirm and Lazar 2019, p. 2, hereafter, WSL 2019). 

The 2019 Editorial announces: “We take that step here….[I]t is time to stop using the term ‘statistically significant’ entirely. …[S]tatistically significant –don’t say it and don’t use it” (WSL 2019, p. 2). Not just outsiders to statistics were surprised. To insiders as well, the 2019 Editorial was sufficiently perplexing for the then ASA President, Karen Kafadar, to call for a New ASA Task Force on Significance Tests and Replicability. 

Many of you have written of instances in which authors and journal editors—and even some ASA members—have mistakenly assumed this editorial represented ASA policy. The mistake is understandable: The editorial was co-authored by an official of the ASA. 

… To address these issues, I hope to establish a working group that will prepare a thoughtful and concise piece … without leaving the impression that p-values and hypothesis tests…have no role in ‘good statistical practice’. (K. Kafadar, President’s Corner, 2019, p. 4) 

This was a key impetus for the JSM panel discussion from which the current paper derives (“P-values and ‘Statistical Significance’: Deconstructing the Arguments”). Kafadar deserves enormous credit for creating the new task force.1 Although the new task force’s report, submitted shortly before the JSM 2020 meeting, has not been disclosed, Kadar’s presentation noted that one of its recommendations is that there be a “disclaimer on all publications, articles, editorials, … authored by ASA Staff”.2 In this case, a disclaimer would have noted that the 2019 Editorial is not ASA policy. Still, given that its authors include ASA officials, it has a great deal of impact. 

We should indeed move away from unthinking and rigid uses of thresholds—not just with significance levels, but also with confidence levels and other quantities. No single statistical quantity from any school, by itself, is an adequate measure of evidence, for any of the many disparate meanings of “evidence” one might adduce. Thus, it is no special indictment of P-values that they fail to supply such a measure. We agree as well that the actual P-value should be reported, as all the founders of tests recommended (see Mayo 2018, Excursion 3 Tour II). But the 2019 Editorial goes much further. In its view: Prespecified P-value thresholds should not be used at all in interpreting results. In other words, the position advanced by the 2019 Editorial, “reject statistical significance”, is not just a word ban but a gatekeeper ban. For example, in order to comply with its recommendations, the FDA would have to end its “long established drug review procedures that involve comparing p-values to significance thresholds for Phase III drug trials” as the authors admit (p. 10). 

Kafadar is right to see the 2019 Editorial as challenging the overall use of hypothesis tests, even though it is not banning P-values. Although P-values can be used as descriptive measures, rather than as tests, when we wish to employ them as tests, we require thresholds. Ideally there are several P-value benchmarks, but even that is foreclosed if we take seriously their view: “[T]he problem is not that of having only two labels. Results should not be trichotomized, or indeed categorized into any number of groups…” (WSL 2019, p. 2). 

The March 2019 Editorial (WSL 2019) also includes a detailed introduction to a special issue of The American Statistician (“Moving to a World beyond p < 0.05”). The position that I will discuss, reject statistical significance, (“don’t say ‘significance’, don’t use P-value thresholds”), is outlined largely in the first two sections of the 2019 Editorial. What are the arguments given for the leap from the reasonable principles of the 2016 ASA Statement to the dramatic “reject statistical significance” position? Do they stand up to principles for good argumentation? 

Continue reading the paper here. Please share your comments.

NOTES:

1 Linda Young, (Co-Chair), Xuming He, (Co-Chair) Yoav Benjamini, Dick De Veaux, Bradley Efron, Scott Evans, Mark Glickman, Barry Graubard, Xiao-Li Meng, Vijay Nair, Nancy Reid, Stephen Stigler, Stephen Vardeman, Chris Wikle, Tommy Wright, Karen Kafadar, Ex-officio. (Kafadar 2020) 

2 Kafadar, K., “P-values: Assumptions, Replicability, ‘Significance’,” slides given in the Contributed Panel: P-Values and “Statistical Significance”: Deconstructing the Arguments at the (virtual) JSM 2020. (August 6, 2020). 

^CITATION: Mayo, D. (2020). Rejecting Statistical Significance Tests: Defanging the Arguments. In JSM Proceedings, Statistical Consulting Section. Alexandria, VA: American Statistical Association. (2020). 236-256.

*Jan 11 update. The ASA executive director, Ron Wasserstein, wants to emphasize that it is leaving to the members of the Task Force when and how to release the report on their own. I do not know if it will do so or if all of the authors will agree to this shift. Personally, I don’t know why the ASA Board would not wish to reveal the recommendations of the Task Force that it created–even without any presumption that it thereby is understood to be a policy document. There can be a clear disclaimer that it is not. The Task Force carried out the work that was asked of them in a timely manner. You can find a statement of the charge given to the Task Force in my comments.

Categories: 2016 ASA Statement on P-values, JSM 2020, replication crisis, statistical significance tests, straw person fallacy | 7 Comments

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