I am grateful to A. Spanos for letting me post a link to his comments on a paper S. Senn shared last week. You can find a pdf of his comments here.
You can read the original Bernard and Copas (2002) article here
Monthly Archives: March 2012
Announcement: Philosophy of Scientific Experiment Conference
Call for papers
PSX Philosophy of Scientific Experimentation 3 (PSX3)
Friday and Saturday, October 5 and 6, 2012
University of Colorado, Boulder
Keynote Speakers: Professor Eric Cornell, University of Colorado, Nobel Prize (Physics, 2001)
Professor Friedrich Steinle, History of Science, University of Berlin
Experiments play essential roles in science. Philosophers of science have emphasized their role in the testing of theories but they also play other important roles. They are, for example, essential in exploring new phenomenological realms and discovering new effects and phenomena. Nevertheless, experiments are still an underrepresented topic in mainstream philosophy of science. This conference on the philosophy of scientific experimentation, the third in a series, is intended to give a home to philosophical interests in, and concerns about, experiment. Among the questions that will be discussed are the following: How is experimental practice organized, around theories or around something else? How independent is experimentation from theories? Does it have a life of its own? Can experiments undermine the threat posed to the objectivity of science by the thesis of theory-ladenness, underdetermination, or the Duhem-Quine thesis? What are the important similarities and differences between experiments in different sciences? What are the experimental strategies scientists use for making sure that their experiments work correctly? How are phenomena discovered or created in the laboratory? Is experimental knowledge epistemically more secure than observational knowledge? Can experiments give us good reasons for belief in theoretical entities? What role do computer simulations play in the assessment of experimental background? How trustworthy are they? Do they warrant the same kind of inferences as experimental knowledge? Are they theory by other means?
Submissions on any aspect of experiment and simulation are welcome. They should be in the form of an extended abstract (1000 words) submitted through EasyChair https://www.easychair.org/conferences/?conf=psx3 Continue reading
Categories: Announcement
Tags: Call for papers, Duhem's problem, experimental inference, philosophy of experiment
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The New York Times Goes to War Against Generic Drug Manufacturers: Schactman
Schachtman gives an interesting legal update today on his blog concerning the issue in my post Generic Drugs Resistant to Lawsuits” (Mar. 22, 2012). I post it here:
The New York Times Goes to War Against Generic Drug Manufacturers
By: Nathan Schachtman, Esq., PC*
Last week marked the launch of a New York Times a rhetorically fevered, legally sophomoric campaign against generic drug preemption. Saturday saw an editorial, “A Bizarre Outcome on Generic Drugs,” New York Times (March 24, 2012), which screamed, “Bizarre”! “Outrageous”!
The New York Times editorialists have their knickers in a knot over the inability of people, who are allegedly harmed by adverse drug reactions from generic medications, to sue the generic manufacturers. The editorial follows a front-page article, from earlier last week, which decried the inability to sue generic drug sellers. See Katie Thomas, “Generic Drugs Proving Resistant to Damage Suits,” New York Times (Mar. 21, 2012).
The Times‘ writers think that it is “bizarre” and “outrageous” that these people are out of court due to federal preemption of state court tort laws that might have provided a remedy.
In particular, the Times suggests that the law is irrational for allowing Ms. Diana Levine to recover against Wyeth for the loss of her arm to gangrene after receiving Phenergan by intravenous push, while another plaintiff, Ms. Schork, cannot recover for a similar injury, from a generic manufacturer of promethazine, the same medication. Wyeth v. Levine, 555 U.S. 555 (2009). See also Brief of Petitioner Wyeth, in Wyeth v. Levine (May 2008).
Of course, both Ms. Levine and Ms. Schork received compensation from their healthcare providers, who deviated from their standard of care when they carelessly injected the medication into arteries, contrary to clear instructions. At the time that Levine received her treatment, the Phenergan package insert contained four separate warnings about the risk of gangrene from improper injection of the medication into an artery. For instance, the “Adverse Reactions” section of the Phenergan label indicated: “INTRA-ARTERIAL INJECTION [CAN] RESULT IN GANGRENE OF THE AFFECTED EXTREMITY.” Continue reading
Categories: Statistics
Tags: evidence based policy, Generic drug, law and statistics, Lawsuit, malpractice, Tort law
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Generic Drugs Resistant to Lawsuits
Waiting for my plane at La Guardia, I see that the NYT has an article on page one about the disparity between suing brand name vs. generic drug makers for failure to adequately warn of serious side effects on their drug labels. Can it be that no one is responsible for monitoring/updating drug label warnings once a drug becomes generic?
Debbie Schork, a deli worker at a supermarket in Indiana, had to have her hand amputated after an emergency room nurse injected her with an anti-nausea drug, causing gangrene. She sued the manufacturer named in the hospital’s records for failing to warn about the risks of injecting it. Her case was quietly thrown out of court last fall.
That result stands in sharp contrast to the highly publicized case of Diana Levine, a professional musician from Vermont. Her hand and forearm were amputated because of gangrene after a physician assistant at a health clinic injected her with the same drug. She sued the drug maker, Wyeth, and won $6.8 million.
The financial outcomes were radically different for one reason: Ms. Schork had received the generic version of the drug, known as promethazine, while Ms. Levine had been given the brand name, Phenergan.
“Explain the difference between the generic and the real one — it’s just a different company making the same thing,” Ms. Schork said.
Objectivity (#5): Three Reactions to the Challenge of Objectivity (in inference):
(1) If discretionary judgments are thought to introduce subjectivity in inference, a classic strategy thought to achieve objectivity is 
to extricate such choices, replacing them with purely formal a priori computations or agreed-upon conventions (see March 14). If leeway for discretion introduces subjectivity, then cutting off discretion must yield objectivity! Or so some argue. Such strategies may be found, to varying degrees, across the different approaches to statistical inference.
The inductive logics of the type developed by Carnap promised to be an objective guide for measuring degrees of confirmation in hypotheses, despite much-discussed problems, paradoxes, and conflicting choices of confirmation logics. In Carnapian inductive logics, initial assignments of probability are based on a choice of language and on intuitive, logical principles. The consequent logical probabilities can then be updated (given the statements of evidence) with Bayes’s Theorem. The fact that the resulting degrees of confirmation are at the same time analytical and a priori—giving them an air of objectivity–reveals the central weakness of such confirmation theories as “guides for life”, e.g., —as guides, say, for empirical frequencies or for finding things out in the real world. Something very similar happens with the varieties of “objective’” Bayesian accounts, both in statistics and in formal Bayesian epistemology in philosophy (a topic to which I will return; if interested, see my RMM contribution).
A related way of trying to remove latitude for discretion might be to define objectivity in terms of the consensus of a specified group, perhaps of experts, or of agents with “diverse” backgrounds. Once again, such a convention may enable agreement yet fail to have the desired link-up with the real world. It would be necessary to show why consensus reached by the particular choice of group (another area for discretion) achieves the learning goals of interest.
Objectivity (#4) and the “Argument From Discretion”
We constantly hear that procedures of inference are inescapably subjective because of the latitude of human judgment as it bears on the collection, modeling, and interpretation of data. But this is seriously equivocal: Being the product of a human subject is hardly the same as being subjective, at least not in the sense we are speaking of—that is, as a threat to objective knowledge. Are all these arguments about the allegedly inevitable subjectivity of statistical methodology rooted in equivocations? I argue that they are!
Insofar as humans conduct science and draw inferences, it is obvious that human judgments and human measurements are involved. True enough, but too trivial an observation to help us distinguish among the different ways judgments should enter, and how, nevertheless, to avoid introducing bias and unwarranted inferences. The issue is not that a human is doing the measuring, but whether we can reliably use the thing being measured to find out about the world.
Categories: Objectivity, Objectivity, Statistics
Tags: dirty hands argument, evidence based policy
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RMM-7: Commentary and Response on Senn published: Special Volume on Stat Scie Meets Phil Sci
Dear Re
ader: My commentary, “How Can We Cultivate Senn’s Ability, Comment on Stephen Senn, ‘You May Believe You are a Bayesian But You’re Probably Wrong’” and Senn’s, “Names and Games, A Reply to Deborah G. Mayo” have been published under the Discussion Section of Rationality, Markets, and Morals.(Special Topic: Statistical Science and Philosophy of Science: Where Do/Should They Meet?”)
I encourage you to submit your comments/exchanges on any of the papers in this special volume [this is the first]. (Information may be found on their webpage [no longer active 3/21/2021]. Questions/Ideas: please write to me at error@vt.edu.)
Blogologue*
Gelman responds on his blog today: “Gelman on Hennig on Gelman on Bayes”.
http://andrewgelman.com/2012/03/gelman-on-hennig-on-gelman-on-bayes/
I invite comments here….
*An ongoing exchange among a group of blogs that remain distinct (just coined)
Categories: Philosophy of Statistics, Statistics, U-Phil
Tags: Andrew Gelman, Bayesian inference, Christian Hennig
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U-PHIL: A Further Comment on Gelman by Christian Hennig (UCL, Statistics)
Comment on Gelman’s
“Induction and Deduction in Bayesian Data Analysis” (RMM)
Dr. Christian Hennig (Senior Lecturer, Department of Statistical Science, University College London)
I have read quite a bit of what Andrew Gelman has written in recent years, including some of his blog. One thing that I find particularly refreshing and important about his approach is that he contrasts the Bayesian and frequentist philosophical conceptions honestly with what happens in the practice of data analysis, which often cannot (or does better not to) proceed according to an inflexible dogmatic book of rules.
I also like the emphasis on the fact that all models are wrong. I personally believe that a good philosophy of statistics should consistently take into account that models are rather tools for thinking than able to “match” reality, and in the vast majority of cases we know clearly that they are wrong (all continuous models are wrong because all observed data are discrete, for a start).
There is, however, one issue on which I find his approach unsatisfactory (or at least not well enough explained), and on which both frequentism and subjective Bayesianism seem superior to me.
Lifting a piece from Spanos’ contribution* will usefully add to the mix
The following two sections from Aris Spanos’ contribution to the RMM volume are relevant to the points raised by Gelman (as regards what I am calling the “two slogans”)**.
6.1 Objectivity in Inference (From Spanos, RMM 2011, pp. 166-7)
The traditional literature seems to suggest that ‘objectivity’ stems from the mere fact that one assumes a statistical model (a likelihood function), enabling one to accommodate highly complex models. Worse, in Bayesian modeling it is often misleadingly claimed that as long as a prior is determined by the assumed statistical model—the so called reference prior—the resulting inference procedures are objective, or at least as objective as the traditional frequentist procedures:
“Any statistical analysis contains a fair number of subjective elements; these include (among others) the data selected, the model assumptions, and the choice of the quantities of interest. Reference analysis may be argued to provide an ‘objective’ Bayesian solution to statistical inference in just the same sense that conventional statistical methods claim to be ‘objective’: in that the solutions only depend on model assumptions and observed data.” (Bernardo 2010, 117)
This claim brings out the unfathomable gap between the notion of ‘objectivity’ as understood in Bayesian statistics, and the error statistical viewpoint. As argued above, there is nothing ‘subjective’ about the choice of the statistical model Mθ(z) because it is chosen with a view to account for the statistical regularities in data z0, and its validity can be objectively assessed using trenchant M-S testing. Model validation, as understood in error statistics, plays a pivotal role in providing an ‘objective scrutiny’ of the reliability of the ensuing inductive procedures.
Mayo, Senn, and Wasserman on Gelman’s RMM** Contribution
Picking up the pieces…
Continuing with our discussion of contributions to the special topic, Statistical Science and Philosophy of Science in Rationality, Markets and Morals (RMM),* I am pleased to post some comments on Andrew **Gelman’s paper “Induction and Deduction in Bayesian Data Analysis”. (More comments to follow—as always, feel free to comment.)
Note: March 9, 2012: Gelman has commented to some of our comments on his blog today: http://andrewgelman.com/2012/03/coming-to-agreement-on-philosophy-of-statistics/
D. Mayo
For now, I will limit my own comments to two: First, a fairly uncontroversial point, while Gelman writes that “Popper has argued (convincingly, in my opinion) that scientific inference is not inductive but deductive,” a main point of my series (Part 1, 2, 3) of “No-Pain” philosophy was that “deductive” falsification involves inductively inferring a “falsifying hypothesis”.
More importantly, and more challengingly, Gelman claims the view he recommends “corresponds closely to the error-statistics idea of Mayo (1996)”. Now the idea that non-Bayesian ideas might afford a foundation for strands of Bayesianism is not as implausible as it may seem. On the face of it, any inference to a claim, whether to the adequacy of a model (for a given purpose), or even to a posterior probability, can be said to be warranted just to the extent that the claim has withstood a severe test (i.e, a test that would, at least with reasonable probability, have discerned a flaw with the claim, were it false). The question is: How well do Gelman’s methods for inferring statistical models satisfy severity criteria? (I’m not sufficiently familiar with his intended applications to say.)
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Categories: Philosophy of Statistics, Statistics, U-Phil
Tags: Andrew Gelman, Larry Wasserman, Stephen Senn, subjective/objective Bayesian
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Statistical Science Court?
Nathan Schactman has an interesting blog post on “Scientific illiteracy among the judiciary”:February 29th, 2012
Ken Feinberg, speaking at a symposium on mass torts, asks what legal challenges do mass torts confront in the federal courts. The answer seems obvious.
Pharmaceutical cases that warrant federal court multi-district litigation (MDL) treatment typically involve complex scientific and statistical issues. The public deserves having MDL cases assigned to judges who have special experience and competence to preside in cases in which these complex issues predominate. There appears to be no procedural device to ensure that the judges selected in the MDL process have the necessary experience and competence, and a good deal of evidence to suggest that the MDL judges are not up to the task at hand.
In the aftermath of the Supreme Court’s decision in Daubert, the Federal Judicial Center assumed responsibility for producing science and statistics tutorials to help judges grapple with technical issues in their cases. The Center has produced videotaped lectures as well as the Reference Manual on Scientific Evidence, now in its third edition. Despite the Center’s best efforts, many federal judges have shown themselves to be incorrigible. It is time to revive the discussions and debates about implementing a “science court.”
I am intrigued to hear Schachtman revive the old and controversial idea of a “science court”, although it has actually never left, but has come up for debate every few years for the past 35 or 40 years! In the 80s, it was a hot topic in the new “science and values” movement, but I do not think it was ever really put to an adequate experimental test. The controversy directly relates to the whole issue of distinguishing evidential and policy issues (in evidence-based policy), Continue reading
Categories: philosophy of science, PhilStatLaw, Statistics
Tags: Allan Mazur, Daubert, evidence based policy, Nathan Schachtman, science court
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MetaBlog: March 2, 2012
Dear Reader: I’ll be traveling, mostly to London, for a couple of weeks, but plan to keep up the blog as usual (semi-irratically regular*); I will mostly keep msc meanderings under the wraps of “pages” (I don’t know if anyone ever reads them, I’m still trying to figure them out actually.)
I will be giving a Popper Lecture at the LSE on Tuesday March 6**. It’s on the philosophy of experiment, no direct discussion of PhilStat; however, I’ve reserved a space Wednesday March 7, mid-day, for anyone who wants to meet to talk about recent PhilStat ponderings, the business on the strong LP, and related issues. If you’re in the neighborhood, write and I’ll give particulars, error@vt.edu.
The Statistics Wars & Their Casualties
LSE PH500 Research Seminar (May 21-June 25, 2020): Controversies in Phil Stat
Summer Seminar 2019 (article)
Experts convene to explore new philosophy of statistics field
Error Statistics Philosophy 