Statistical Modeling, Causal Inference, and Social Science

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2021-07-12 15:00:09

The statement . . . describes establishment of the task force to “address concerns that a 2019 editorial in The American Statistician (an ASA journal) might be mistakenly interpreted as official ASA policy. (The 2019 editorial recommended eliminating the use of ‘p<0.05’ and ‘statistically significant’ in statistical analysis.)” The authors go on to more specifically identify the purpose of the statement as “two-fold: to clarify that the use of P-values and significance testing, properly applied and interpreted, are important tools that should not be abandoned, and to briefly set out some principles of sound statistical inference that may be useful to the scientific community.”

The task force includes several prominent academic and government statisticians (including lots of people I know personally), and both of its goals—clarifying the ASA’s official position and giving its own recommendations—seem valuable to me.

Goal #1—clarifying the ASA’s official position—was simple but it still had to be done. A few years ago the ASA had a committee that in 2016 released a statement on statistical significance and p-values. I was on this committee, along with other difficult people such as Sander Greenland—I mean “difficult” in a good way here!—and I agreed with much but not all of the statement. My response, “The problems with p-values are not just with p-values,” was published here. Various leading statisticians disagreed more strongly with that committee’s report. I think it’s fair to say that that earlier report is not official ASA policy, and it’s good for this new report to clarify this point.