I. Doubt is Their Product is the title of a (2008) book by David Michaels, Assistant Secretary for OSHA from 2009-2017. I first mentioned it on this blog back in 2011 (“Will the Real Junk Science Please Stand Up?) The expression is from a statement by a cigarette executive (“doubt is our product”), and the book’s thesis is explained in its subtitle: How Industry’s Assault on Science Threatens Your Health. Imagine you have just picked up a book, published in 2020: Bad Statistics is Their Product. Is the author writing about how exaggerating bad statistics may serve in the interest of denying well-established risks? [Interpretation A]. Or perhaps she’s writing on how exaggerating bad statistics serves the interest of denying well-established statistical methods? [Interpretation B]. Both may result in distorting science and even in dismantling public health safeguards–especially if made the basis of evidence policies in agencies. A responsible philosopher of statistics should care. Continue reading
slides
Bad Statistics is Their Product: Fighting Fire With Fire (ii)
The Statistics Wars: Errors and Casualties
Had I been scheduled to speak later at the 12th MuST Conference & 3rd Workshop “Perspectives on Scientific Error” in Munich, rather than on day 1, I could have (constructively) illustrated some of the errors and casualties by reference to a few of the conference papers that discussed significance tests. (Most gave illuminating discussions of such topics as replication research, the biases that discredit meta-analysis, statistics in the law, formal epistemology [i]). My slides follow my abstract. Continue reading →
Replication Crises and the Statistics Wars: Hidden Controversies
Below are the slides from my June 14 presentation at the X-Phil conference on Reproducibility and Replicability in Psychology and Experimental Philosophy at University College London. What I think must be examined seriously are the “hidden” issues that are going unattended in replication research and related statistics wars. An overview of the “hidden controversies” are on slide #3. Although I was presenting them as “hidden”, I hoped they wouldn’t be quite as invisible as I found them through the conference. (Since my talk was at the start, I didn’t know what to expect–else I might have noted some examples that seemed to call for further scrutiny). Exceptions came largely (but not exclusively) from a small group of philosophers (me, Machery and Fletcher). Then again,there were parallel sessions, so I missed some. However, I did learn something about X-phil, particularly from the very interesting poster session [1]. This new area should invite much, much more scrutiny of statistical methodology from philosophers of science.
[1] The women who organized and ran the conference did an excellent job: Lara Kirfel, a psychology PhD student at UCL, and Pascale Willemsen from Ruhr University.
Your data-driven claims must still be probed severely
Below are the slides from my talk today at Columbia University at a session, Philosophy of Science and the New Paradigm of Data-Driven Science, at an American Statistical Association Conference on Statistical Learning and Data Science/Nonparametric Statistics. Todd was brave to sneak in philosophy of science in an otherwise highly mathematical conference.
Philosophy of Science and the New Paradigm of Data-Driven Science : (Room VEC 902/903)
Organizer and Chair: Todd Kuffner (Washington U)
- Deborah Mayo (Virginia Tech) “Your Data-Driven Claims Must Still be Probed Severely”
- Ian McKeague (Columbia) “On the Replicability of Scientific Studies”
- Xiao-Li Meng (Harvard) “Conducting Highly Principled Data Science: A Statistician’s Job and Joy

The Statistics Wars & Their Casualties
Blog links (references)
Reviews of Statistical Inference as Severe Testing (SIST)
- P. Bandyopadhyay (2019) Notre Dame Philosophical Reviews
- C. Hennig (2019) Statistical Modeling, Causal. Inference, and Social Science blog
- A. Spanos (2019) OEconomia: History, Methodology, Philosophy
- R. Cousins 2020 (Preprint)
- S. Fletcher (2020) Philosophy of Science
- B. Haig (2020) Methods in Psychology
- C. Mayo-Wilson (2020 forthcoming) Philosophical Review
- T. Sterkenburg (2020) Journal for General Philosophy of Science
Interviews & Debates on PhilStat (2020)
- The Statistics Debate!with Jim Berger, Deborah Mayo, David Trafimow & Dan Jeske, moderator (10/15/20)
- The Filter podcast with Matt Asher (11/23/20)
- Philosophy of Data Science Series Keynote Episode 1: Revolutions, Reforms, and Severe Testing in Data Science with Glen Wright Colopy (11/24/20)
- Philosophy of Data Science Series Keynote Episode 2: The Philosophy of Science & Statistics with Glen Wright Colopy (12/01/20)
Interviews on PhilStat (2019)
Top Posts & Pages
- 2023 Syllabus for Philosophy of Inductive-Statistical Inference
- S. Senn: "Error point: The importance of knowing how much you don’t know" (guest post)
- Spurious Correlations: Death by getting tangled in bedsheets and the consumption of cheese! (Aris Spanos)
- Does statistics have an ontology? Does it need one? (draft 2)
- The First 2023 Act of Stat Activist Watch: Statistics 'for the people'
- Nathan Schactman: Of Significance, Error, Confidence, and Confusion – In the Law and In Statistical Practice (Guest Post)
- I'm teaching a New Intro to PhilStat Course Starting Wednesday:
- The Meaning of My Title: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars
- Little Bit of Logic (5 mini problems for the reader)
- Erich Lehmann's 100 Birthday: Neyman Pearson vs Fisher on P-values
Conferences & Workshops
RMM Special Topic
Mayo & Spanos, Error Statistics
My Websites
Recent Posts: PhilStatWars
THE STATISTICS WARS AND THEIR CASUALTIES VIDEOS & SLIDES FROM SESSIONS 3 & 4
Final session: The Statistics Wars and Their Casualties: 8 December, Session 4
SCHEDULE: The Statistics Wars and Their Casualties: 1 Dec & 8 Dec: Sessions 3 & 4
WORKSHOP
The Statistics Wars and Their Casualties Videos & Slides from Sessions 1 & 2
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