Like most wars, the Statistics Wars continues to have casualties. Some of the reforms thought to improve reliability and replication may actually create obstacles to methods known to improve on reliability and replication. At each one of our meeting of the Phil Stat Forum: “The Statistics Wars and Their Casualties,” I take 5 -10 minutes to draw out a proper subset of casualties associated with the topic of the presenter for the day. (The associated workshop that I have been organizing with Roman Frigg at the London School of Economics (CPNSS) now has a date for a hoped for in-person meeting in London: 24-25 September 2021.) Of course we’re interested not just in casualties but in positive contributions, though what counts as a casualty and what a contribution is itself a focus of philosophy of statistics battles.
At our last meeting, Thursday, 25 March, Mark Burgman, Director of the Centre for Environmental Policy at Imperial College London and Editor-in-Chief of the journal Conservation Biology, spoke on “How should applied science journal editors deal with statistical controversies?“. His slides are here: (pdf). The casualty I focussed on is how the statistics wars may put journal editors in positions of conflicts of interest that can get in the way of transparency and avoidance of bias. I presented it in terms of 4 questions (nothing to do with the fact that it’s currently Passover):
D. Mayo’s Casualties: Intellectual Conflicts of Interest: Questions for Burgman
- In an applied field such as conservation science, where statistical inferences often are the basis for controversial policy decisions, should editors and editorial policies avoid endorsing one side of the long-standing debate revolving around statistical significance tests? Or should they adopt and promote a favored methodology?
- If editors should avoid taking a side in setting author’s guidelines and reviewing papers, what policies should be adopted to avoid deferring to the calls of those wanting them to change their author’s guidelines? Have you ever been encouraged to do so?
- If one has a strong philosophical statistical standpoint and a strong interest in persuading others to accept it, does it create a conflict of interest, if that person has power to enforce that philosophy (especially in a group already driven by perverse incentives)? If so, what is your journal doing to take account of and prevent conflicts of interest?
- What do you think of the March 2019 Editorial of The American Statistician (Wasserstein et al., 2019) Don’t say “statistical significance” and don’t use predesignated p-value thresholds in interpreting data (e.g., .05, .01, .005).
(While not an ASA policy document, Wasserstein’s status as ASA executive director gave it a lot of clout. Should he have issued a disclaimer that the article only represents the authors’ views?) 
This is the first of some posts on intellectual conflicts of interest that I’ll be writing shortly. 
Mark Burgman’s presentation (Link)
D. Mayo’s Casualties (Link)
 For those who don’t know the story: Because no disclaimer was issued, the ASA Board appointed a new task force on Statistical Significance and Reproducibility in 2019 to provide recommendations. These have thus far not been made public. For the background, see this post.
Burgman said that he had received a request to follow the “don’t say significance, don’t use P-value thresholds”, but upon considering it with colleagues, they decided against it. Why not include, as part of journal information shared with authors, that the editors consider it important to retain a variety of statistical methodologies–correctly used–and have explicitly rejected the call to ban any of them (even if they come with official association letterhead).
 WordPress has just sprung a radical change on bloggers, and as I haven’t figured it out yet, and my blog assistant is unavailable, I’ve cut this post short.