Nearly 3 months ago I tweeted “Stat people: shouldn’t they be testing a largish random sample of people [w/o symptoms] to assess rates, alert those infected, rather than only high risk, symptomatic people, in the U.S.?” I was surprised that nearly all the stat and medical people I know expressed the view that it wouldn’t be feasible or even very informative. Really? Granted, testing was and is limited, but had it been made a priority, it could have been done. In the new issue of Significance (June 2020) that I just received, James J. Cochran writes “on the importance of testing a random sample.” 
Author Archives: Mayo
Today is Allan Birnbaum’s birthday. In honor of his birthday, I’m posting the articles in the Synthese volume that was dedicated to his memory in 1977. The editors describe it as their way of “paying homage to Professor Birnbaum’s penetrating and stimulating work on the foundations of statistics”. I had posted the volume before, but there are several articles that are very worth rereading. I paste a few snippets from the articles by Giere and Birnbaum. If you’re interested in statistical foundations, and are unfamiliar with Birnbaum, here’s a chance to catch up. (Even if you are, you may be unaware of some of these key papers.) Continue reading
Ship StatInfasST will embark on a new journey from 21 May – 18 June, a graduate research seminar for the Philosophy, Logic & Scientific Method Department at the LSE, but given the pandemic has shut down cruise ships, it will remain at dock in the U.S. and use zoom. If you care to follow any of the 5 sessions, nearly all of the materials will be linked here collected from excerpts already on this blog. If you are interested in observing on zoom beginning 28 May, please follow the directions here.
For the updated schedule, see the seminar web page.
Topic: Current Controversies in Phil Stat
(LSE, Remote 10am-12 EST, 15:00 – 17:00 London time; Thursdays 21 May-18 June) Continue reading
Final part of B. Haig’s ‘What can psych stat reformers learn from the error-stat perspective?’ (Bayesian stats)
Here’s the final part of Brian Haig’s recent paper ‘What can psychology’s statistics reformers learn from the error-statistical perspective?’ in Methods in Psychology 2 (Nov. 2020). The full article, which is open access, is here. I will make some remarks in the comments.
5. The error-statistical perspective and the nature of science
As noted at the outset, the error-statistical perspective has made significant contributions to our philosophical understanding of the nature of science. These are achieved, in good part, by employing insights about the nature and place of statistical inference in experimental science. The achievements include deliberations on important philosophical topics, such as the demarcation of science from non-science, the underdetermination of theories by evidence, the nature of scientific progress, and the perplexities of inductive inference. In this article, I restrict my attention to two such topics: The process of falsification and the structure of modeling.
5.1. Falsificationism Continue reading
Part 2 of B. Haig’s ‘What can psych stat reformers learn from the error-stat perspective?’ (Bayesian stats)
Here’s a picture of ripping open the first box of (rush) copies of Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars*, and here’s a continuation of Brian Haig’s recent paper ‘What can psychology’s statistics reformers learn from the error-statistical perspective?’ in Methods in Psychology 2 (Nov. 2020). Haig contrasts error statistics, the “new statistics”, and Bayesian statistics from the perspective of the statistics wars in psychology. The full article, which is open access, is here. I will make several points in the comments.
4. Bayesian statistics
Despite its early presence, and prominence, in the history of statistics, the Bayesian outlook has taken an age to assert itself in psychology. However, a cadre of methodologists has recently advocated the use of Bayesian statistical methods as a superior alternative to the messy frequentist practice that dominates psychology’s research landscape (e.g., Dienes, 2011; Kruschke and Liddell, 2018; Wagenmakers, 2007). These Bayesians criticize NHST, often advocate the use of Bayes factors for hypothesis testing, and rehearse a number of other well-known Bayesian objections to frequentist statistical practice. Continue reading
This is the title of Brian Haig’s recent paper in Methods in Psychology 2 (Nov. 2020). Haig is a professor emeritus of psychology at the University of Canterbury. Here he provides both a thorough and insightful review of my book Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (CUP, 2018) as well as an excellent overview of the high points of today’s statistics wars and the replication crisis, especially from the perspective of psychology. I’ll excerpt from his article in a couple of posts. The full article, which is open access, is here.
Abstract: In this article, I critically evaluate two major contemporary proposals for reforming statistical thinking in psychology: The recommendation that psychology should employ the “new statistics” in its research practice, and the alternative proposal that it should embrace Bayesian statistics. I do this from the vantage point of the modern error-statistical perspective, which emphasizes the importance of the severe testing of knowledge claims. I also show how this error-statistical perspective improves our understanding of the nature of science by adopting a workable process of falsification and by structuring inquiry in terms of a hierarchy of models. Before concluding, I briefly discuss the importance of the philosophy of statistics for improving our understanding of statistical thinking.
Keywords: The error-statistical perspective, The new statistics, Bayesian statistics, Falsificationism, Hierarchy of models, Philosophy of statistics Continue reading
S. Senn: Randomisation is not about balance, nor about homogeneity but about randomness (Guest Post)
The intellectual illness of clinical drug evaluation that I have discussed here can be cured, and it will be cured when we restore intellectual primacy to the questions we ask, not the methods by which we answer them. Lewis Sheiner1
Cause for concern
In their recent essay Causal Evidence and Dispositions in Medicine and Public Health2, Elena Rocca and Rani Lill Anjum challenge, ‘the epistemic primacy of randomised controlled trials (RCTs) for establishing causality in medicine and public health’. That an otherwise stimulating essay by two philosophers, experts on causality, which makes many excellent points on the nature of evidence, repeats a common misunderstanding about randomised clinical trials, is grounds enough for me to address this topic again. Before, however, explaining why I disagree with Rocca and Anjum on RCTs, I want to make clear that I agree with much of what they say. I loathe these pyramids of evidence, beloved by some members of the evidence-based movement, which have RCTs at the apex or possibly occupying a second place just underneath meta-analyses of RCTs. In fact, although I am a great fan of RCTs and (usually) of intention to treat analysis, I am convinced that RCTs alone are not enough. My thinking on this was profoundly affected by Lewis Sheiner’s essay of nearly thirty years ago (from which the quote at the beginning of this blog is taken). Lewis was interested in many aspects of investigating the effects of drugs and would, I am sure, have approved of Rocca and Anjum’s insistence that there are many layers of understanding how and why things work, and that means of investigating them may have to range from basic laboratory experiments to patient narratives via RCTs. Rocca and Anjum’s essay provides a good discussion of the various ‘causal tasks’ that need to be addressed and backs this up with some excellent examples. Continue reading
As much as doctors and hospitals are raising alarms about a shortage of ventilators for Covid-19 patients, some doctors have begun to call for entirely reassessing the standard paradigm for their use–according to a cluster of articles to appear in the last week. “What’s driving this reassessment is a baffling observation about Covid-19: Many patients have blood oxygen levels so low they should be dead. But they’re not gasping for air, their hearts aren’t racing, and their brains show no signs of blinking off from lack of oxygen.” Within that group of patients, some doctors wonder if the standard use of mechanical ventilators does more harm than good. The issue is controversial; I’ll just report what I find in the articles over the past week. Please share ongoing updates in the comments. Continue reading
A. Spanos: Isaac Newton and his two years in quarantine: how science could germinate in bewildering ways (Guest post)
Wilson Schmidt Professor of Economics
Department of Economics
Beyond the plenitude of misery and suffering that pandemics bring down on humanity, occasionally they contribute to the betterment of humankind by (inadvertently) boosting creative activity that leads to knowledge, and not just in epidemiology. A case in point is that of Isaac Newton and the pandemic of 1665-6. Continue reading
My “April 1” posts for the past 8 years have been so close to the truth or possible truth that they weren’t always spotted as April Fool’s pranks, which is what made them genuine April Fool’s pranks. (After a few days I either labeled them as such, e.g., “check date!”, or revealed it in a comment). Given the level of current chaos and stress, I decided against putting up a planned post for today, so I’m just doing a memory lane of past posts. (You can tell from reading the comments which had most people fooled.) Continue reading
Q. Was it a mistake to quarantine the passengers aboard the Diamond Princess in Japan?
A. The original statement, which is not unreasonable, was that the best thing to do with these people was to keep them safely quarantined in an infection-control manner on the ship. As it turned out, that was very ineffective in preventing spread on the ship. So the quarantine process failed. I mean, I’d like to sugarcoat it and try to be diplomatic about it, but it failed. I mean, there were people getting infected on that ship. So something went awry in the process of quarantining on that ship. I don’t know what it was, but a lot of people got infected on that ship. (Dr. A Fauci, Feb 17, 2020)
This is part of an interview of Dr. Anthony Fauci, the coronavirus point person we’ve been seeing so much of lately. Fauci has been the director of the National Institute of Allergy and Infectious Diseases since all the way back to 1984! You might find his surprise surprising. Even before getting our recent cram course on coronavirus transmission, tales of cruises being hit with viral outbreaks are familiar enough. The horror stories from passengers on the floating petri dish were well known by this Feb 17 interview. Even if everything had gone as planned, the quarantine was really only for the (approximately 3700) passengers because the 1000 or so crew members still had to run the ship, as well as cook and deliver food to the passenger’s cabins. Moreover, the ventilation systems on cruise ships can’t filter out particles smaller than 5000 or 1000 nanometers. Continue reading
Correcting errors about corrected estimates
Randomised clinical trials are a powerful tool for investigating the effects of treatments. Given appropriate design, conduct and analysis they can deliver good estimates of effects. The key feature is concurrent control. Without concurrent control, randomisation is impossible. Randomisation is necessary, although not sufficient, for effective blinding. It also is an appropriate way to deal with unmeasured predictors, that is to say suspected but unobserved factors that might also affect outcome. It does this by ensuring that, in the absence of any treatment effect, the expected value of variation between and within groups is the same. Furthermore, probabilities regarding the relative variation can be delivered and this is what is necessary for valid inference. Continue reading
I will run a graduate Research Seminar at the LSE on Thursdays from May 21-June 18:
Notre Dame Philosophical Reviews is a leading forum for publishing reviews of books in philosophy. The philosopher of statistics, Prasanta Bandyopadhyay, published a review of my book Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP)(SIST) in this journal, and I very much appreciate his doing so. Here I excerpt from his review, and respond to a cluster of related criticisms in order to avoid some fundamental misunderstandings of my project. Here’s how he begins:
In this book, Deborah G. Mayo (who has the rare distinction of making an impact on some of the most influential statisticians of our time) delves into issues in philosophy of statistics, philosophy of science, and scientific methodology more thoroughly than in her previous writings. Her reconstruction of the history of statistics, seamless weaving of the issues in the foundations of statistics with the development of twentieth-century philosophy of science, and clear presentation that makes the content accessible to a non-specialist audience constitute a remarkable achievement. Mayo has a unique philosophical perspective which she uses in her study of philosophy of science and current statistical practice.
I regard this as one of the most important philosophy of science books written in the last 25 years. However, as Mayo herself says, nobody should be immune to critical assessment. This review is written in that spirit; in it I will analyze some of the shortcomings of the book.
This is a belated birthday post for R.A. Fisher (17 February, 1890-29 July, 1962)–it’s a guest post from earlier on this blog by Aris Spanos.
Happy belated birthday to R.A. Fisher!
‘R. A. Fisher: How an Outsider Revolutionized Statistics’
by Aris Spanos
Few statisticians will dispute that R. A. Fisher (February 17, 1890 – July 29, 1962) is the father of modern statistics; see Savage (1976), Rao (1992). Inspired by William Gosset’s (1908) paper on the Student’s t finite sampling distribution, he recast statistics into the modern model-based induction in a series of papers in the early 1920s. He put forward a theory of optimal estimation based on the method of maximum likelihood that has changed only marginally over the last century. His significance testing, spearheaded by the p-value, provided the basis for the Neyman-Pearson theory of optimal testing in the early 1930s. According to Hald (1998) Continue reading
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
My new paper, “P Values on Trial: Selective Reporting of (Best Practice Guides Against) Selective Reporting” is out in Harvard Data Science Review (HDSR). HDSR describes itself as a A Microscopic, Telescopic, and Kaleidoscopic View of Data Science. The editor-in-chief is Xiao-li Meng, a statistician at Harvard. He writes a short blurb on each article in his opening editorial of the issue. Continue reading
‘The term “point estimation” made Fisher nervous, because he associated it with estimation without regard to accuracy, which he regarded as ridiculous.’ Jimmy Savage [1, p. 453]
First things second
The classic text by David Cox and David Hinkley, Theoretical Statistics (1974), has two extremely interesting features as regards estimation. The first is in the form of an indirect, implicit, message and the second explicit and both teach that point estimation is far from being an obvious goal of statistical inference. The indirect message is that the chapter on point estimation (chapter 8) comes after that on interval estimation (chapter 7). This may puzzle the reader, who may anticipate that the complications of interval estimation would be handled after the apparently simpler point estimation rather than before. However, with the start of chapter 8, the reasoning is made clear. Cox and Hinkley state: Continue reading
Aris Spanos was asked to review my Statistical Inference as Severe Testing: how to Get Beyond the Statistics Wars (CUP, 2018), but he was to combine it with a review of the re-issue of Ian Hacking’s classic Logic of Statistical Inference. The journal is OEconomia: History, Methodology, Philosophy. Below are excerpts from his discussion of my book (pp. 843-860). I will jump past the Hacking review, and occasionally excerpt for length.To read his full article go to external journal pdf or stable internal blog pdf. Continue reading
Remember when I wrote to the National Academy of Science (NAS) in September pointing out mistaken definitions of P-values in their document on Reproducibility and Replicability in Science? (see my 9/30/19 post). I’d given up on their taking any action, but yesterday I received a letter from the NAS Senior Program officer:
Dear Dr. Mayo,
I am writing to let you know that the Reproducibility and Replicability in Science report has been updated in response to the issues that you have raised.
Two footnotes, on pages
3135 and 221, highlight the changes. The updated report is available from the following link: NEW 2020 NAS DOC
Thank you for taking the time to reach out to me and to Dr. Fineberg and letting us know about your concerns.
With kind regards and wishes of a happy 2020,
Jennifer Heimberg, Ph.D.
Senior Program Officer
The National Academies of Sciences, Engineering, and Medicine