philosophy of science

Good Scientist Badge of Approval?

In an attempt to fix the problem of “unreal” results in science some have started a “reproducibility initiative”. Think of the incentive for being explicit about how the results were obtained the first time….But would researchers really pay to have their potential errors unearthed in this way?  Even for a “good scientist” badge of approval?

August 14, 2012

Fixing Science’s Problem of ‘Unreal’ Results: “Good Scientist: You Get a Badge!”

Carl Zimmer, Slate

As a young biologist, Elizabeth Iorns did what all young biologists do: She looked around for something interesting to investigate. Having earned a Ph.D. in cancer biology in 2007, she was intrigued by a paper that appeared the following year in Nature. Biologists at the University of California-Berkeley linked a gene called SATB1 to cancer. They found that it becomes unusually active in cancer cells and that switching it on in ordinary cells made them cancerous. The flipside proved true, too: Shutting down SATB1 in cancer cells returned them to normal. The results raised the exciting possibility that SATB1 could open up a cure for cancer. So Iorns decided to build on the research.

There was just one problem. As her first step, Iorns tried replicate the original study. She couldn’t. Boosting SATB1 didn’t make cells cancerous, and shutting it down didn’t make the cancer cells normal again.

For some years now, scientists have gotten increasingly worried about replication failures. In one recent example, NASA made a headline-grabbing announcement in 2010 that scientists had found bacteria that could live on arsenic—a finding that would require biology textbooks to be rewritten. At the time, many experts condemned the paper as a poor piece of science that shouldn’t have been published. This July, two teams of scientists reported that they couldn’t replicate the results. Continue reading

Categories: philosophy of science, Philosophy of Statistics | Tags: , , , | 12 Comments

Clark Glymour: The Theory of Search Is the Economics of Discovery (part 1)

The Theory of Search Is the Economics of Discovery:
Some Thoughts Prompted by Sir David Hendry’s Essay  *
in Rationality, Markets and Morals (RMM) Special Topic:
Statistical Science and Philosophy of Science

Part 1 (of 2)

Professor Clark Glymour

Alumni University Professor
Department of Philosophy[i]
Carnegie Mellon University

Professor Hendry* endorses a distinction between the “context of discovery” and the “context of evaluation” which he attributes to Herschel and to Popper and could as well have attributed also to Reichenbach and to most contemporary methodological commentators in the social sciences. The “context” distinction codes two theses.

1.“Discovery” is a mysterious psychological process of generating hypotheses; “evaluation” is about the less mysterious process of warranting them.

2. Of the three possible relations with data that could conceivably warrant a hypothesis—how it was generated, its explanatory connections with the data used to generate it, and its predictions—only the last counts.

Einstein maintained the first but not the second. Popper maintained the first but that nothing warrants a hypothesis.  Hendry seems to maintain neither–he has a method for discovery in econometrics, a search procedure briefly summarized in the second part of his essay, which is not evaluated by forecasts. Methods may be esoteric but they are not mysterious. And yet Hendry endorses the distinction. Let’s consider it.

As a general principle rather than a series of anecdotes, the distinction between discovery and justification or evaluation has never been clear and what has been said in its favor of its implied theses has not made much sense, ever. Let’s start with the father of one of Hendry’s endorsers, William Herschel. William Herschel discovered Uranus, or something. Actually, the discovery of the planet Uranus was a collective effort with, subject to vicissitudes of error and individual opinion, was a rational search strategy. On March 13, 1781, in the course of a sky survey for double stars Hershel reports in his journal the observation of a “nebulous star or perhaps a comet.”  The object came to his notice how it appeared through the telescope, perhaps the appearance of a disc. Herschel changed the magnification of his telescope, and finding that the brightness of the object changed more than the brightness of fixed stars, concluded he had seen a comet or “nebulous star.”  Observations that, on later nights, it had moved eliminated the “nebulous star” alternative and Herschel concluded that he had seen a comet. Why not a planet? Because lots of comets had been hitherto observed—Edmund Halley computed orbits for half a dozen including his eponymous comet—but never a planet.  A comet was much the more likely on frequency grounds. Further, Herschel had made a large error in his estimate of the distance of the body based on parallax values using his micrometer.  A planet could not be so close.

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“Always the last place you look!”

“Always the last place you look!”

This gets to a distinction I have tried to articulate, between explaining a known effect (like looking for a known object), and searching for an unknown effect (that may well not exist). In the latter, possible effects of “selection” or searching need to be taken account of. Of course, searching for the Higgs is akin to the latter, not the former, hence the joke in the recent New Yorker cartoon.

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Peter Grünwald: Follow-up on Cherkassky’s Comments

Peter Grünwald

Peter Grünwald

A comment from Professor Peter Grünwald

Head, Information-theoretic Learning Group, Centrum voor Wiskunde en Informatica (CWI)
Part-time full professor  at Leiden University.

This is a follow-up on Vladimir Cherkassky’s comments on Deborah’s blog. First of all let me thank Vladimir for taking the time to clarify his position. Still, there’s one issue where we disagree and which, at the same time, I think, needs clarification, so I decided to write this follow-up.[related posts 1]

The issue is about how central VC (Vapnik-Chervonenkis)-theory is to inductive inference.

I agree with Vladimir that VC-theory is one of the most important achievements in the field ever, and indeed, that it fundamentally changed our way of thinking about learning from data. Yet I also think that there are many problems of inductive inference to which it has no direct bearing. Some of these are concerned with hypothesis testing, but even when one is concerned with prediction accuracy – which Vladimir considers the basic goal – there are situations where I do not see how it plays a direct role. One of these is sequential prediction with log-loss or its generalization, Cover’s loss. This loss function plays a fundamental role in (1) language modeling, (2) on-line data compression, (3a) gambling and (3b) sequential investment on the stock market (here we need Cover’s loss). [a superquick intro to log-loss as well as some references are given below under [A]; see also my talk at the Ockham workshop (slides 16-26 about weather forecasting!) )

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Is Particle Physics Bad Science?

I suppose[ed] this was somewhat of a joke from the ISBA, prompted by Dennis Lindley, but as I [now] accord the actual extent of jokiness to be only ~10%, I’m sharing it on the blog [i].  Lindley (according to O’Hagan) wonders why scientists require so high a level of statistical significance before claiming to have evidence of a Higgs boson.  It is asked: “Are the particle physics community completely wedded to frequentist analysis?  If so, has anyone tried to explain what bad science that is?”

Bad science?   I’d really like to understand what these representatives from the ISBA would recommend, if there is even a shred of seriousness here (or is Lindley just peeved that significance levels are getting so much press in connection with so important a discovery in particle physics?)

Well, read the letter and see what you think.

On Jul 10, 2012, at 9:46 PM, ISBA Webmaster wrote:

Dear Bayesians,

A question from Dennis Lindley prompts me to consult this list in search of answers.

We’ve heard a lot about the Higgs boson.  The news reports say that the LHC needed convincing evidence before they would announce that a particle had been found that looks like (in the sense of having some of the right characteristics of) the elusive Higgs boson.  Specifically, the news referred to a confidence interval with 5-sigma limits.

Now this appears to correspond to a frequentist significance test with an extreme significance level.  Five standard deviations, assuming normality, means a p-value of around 0.0000005.  A number of questions spring to mind.

1.  Why such an extreme evidence requirement?  We know from a Bayesian  perspective that this only makes sense if (a) the existence of the Higgs  boson (or some other particle sharing some of its properties) has extremely small prior probability and/or (b) the consequences of erroneously announcing its discovery are dire in the extreme.  Neither seems to be the case, so why  5-sigma?

2.  Rather than ad hoc justification of a p-value, it is of course better to do a proper Bayesian analysis.  Are the particle physics community completely wedded to frequentist analysis?  If so, has anyone tried to explain what bad science that is? Continue reading

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Vladimir Cherkassky Responds on Foundations of Simplicity

I thank Dr. Vladimir Cherkassky for taking up my general invitation to comment. I don’t have much to add to my original post[i], except to make two corrections at the end of this post.  I invite readers’ comments.

Vladimir Cherkassky

As I could not participate in the discussion session on Sunday, I would like to address several technical issues and points of disagreement that became evident during this workshop. All opinions are mine, and may not be representative of the “machine learning community.” Unfortunately, the machine learning community at large is not very much interested in the philosophical and methodological issues. This breeds a lot of fragmentation and confusion, as evidenced by the existence of several technical fields: machine learning, statistics, data mining, artificial neural networks, computational intelligence, etc.—all of which are mainly concerned with the same problem of estimating good predictive models from data.

Occam’s Razor (OR) is a general metaphor in the philosophy of science, and it has been discussed for ages. One of the main goals of this workshop was to understand the role of OR as a general inductive principle in the philosophy of science and, in particular, its importance in data-analytic knowledge discovery for statistics and machine learning.

Data-analytic modeling is concerned with estimating good predictive models from finite data samples. This is directly related to the philosophical problem of inductive inference. The problem of learning (generalization) from finite data had been formally investigated in VC-theory ~ 40 years ago. This theory starts with a mathematical formulation of the problem of learning from finite samples, without making any assumptions about parametric distributions. This formalization is very general and relevant to many applications in machine learning, statistics, life sciences, etc. Further, this theory provides necessary and sufficient conditions for generalization. That is, a set of admissible models (hypotheses about the data) should be constrained, i.e., should have finite VC-dimension. Therefore, any inductive theory or algorithm designed to explain the data should satisfy VC-theoretical conditions. Continue reading

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Comment on Falsification

The comment box was too small for my reply to Sober on falsification, so I will post it here:

I want to understand better Sober’s position on falsification. A pervasive idea to which many still subscribe, myself included, is that the heart of what makes inquiry scientific is the critical attitude: that if a claim or hypothesis or model fails to stand up to critical scrutiny it is rejected as false, and not propped up with various “face-saving” devices. Now

Sober writes “I agree that we can get rid of models that deductively entail (perhaps with the help of auxiliary assumptions) observational outcomes that do not happen.  But as soon as the relation is nondeductive, is there ‘falsification’”?

My answer is yes, else we could scarcely retain the critical attitude for any but the most trivial scientific claims. While at one time philosophers imagined that “observational reports” were given, and could therefore form the basis for a deductive falsification of scientific claims, certainly since Popper, Kuhn and the rest of the post-positivists, we recognize that observations are error prone, as are appeals to auxiliary hypotheses. Here is Popper: Continue reading

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Elliott Sober Responds on Foundations of Simplicity

Here are a few comments on your recent blog about my ideas on parsimony.  Thanks for inviting me to contribute!

You write that in model selection, “’parsimony fights likelihood,’ while, in adequate evolutionary theory, the two are thought to go hand in hand.”  The second part of this statement isn’t correct.  There are sufficient conditions (i.e., models of the evolutionary process) that entail that parsimony and maximum likelihood are ordinally equivalent, but there are cases in which they are not.  Biologists often have data sets in which maximum parsimony and maximum likelihood disagree about which phylogenetic tree is best.

You also write that “error statisticians view hypothesis testing as between exhaustive hypotheses H and not-H (usually within a model).”  I think that the criticism of Bayesianism that focuses on the problem of assessing the likelihoods of “catch-all hypotheses” applies to this description of your error statistical philosophy.  The General Theory of Relativity, for example, may tell us how probable a set of observations is, but its negation does not.  I note that you have “usually within a model” in parentheses.  In many such cases, two alternatives within a model will not be exhaustive even within the confines of a model and of course they won’t be exhaustive if we consider a wider domain.

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More from the Foundations of Simplicity Workshop*

*See also earlier posts from the CMU workshop here and here.

Elliott Sober has been writing on simplicity for a long time, so it was good to hear his latest thinking. If I understood him, he continues to endorse a comparative likelihoodist account, but he allows that, in model selection, “parsimony fights likelihood,” while, in adequate evolutionary theory, the two are thought to go hand in hand. Where it seems needed, therefore, he accepts a kind of “pluralism”. His discussion of the rival models in evolutionary theory and how they may give rise to competing likelihoods (for “tree taxonomies”) bears examination in its own right, but being in no position to accomplish this, I shall limit my remarks to the applicability of Sober’s insights (as my notes reflect them) to the philosophy of statistics and statistical evidence.

1. Comparativism:  We can agree that a hypothesis is not appraised in isolation, but to say that appraisal is “contrastive” or “comparativist” is ambiguous. Error statisticians view hypothesis testing as between exhaustive hypotheses H and not-H (usually within a model), but deny that the most that can be said is that one hypothesis or model is comparatively better than another, among a group of hypotheses that is to be delineated at the outset. There’s an important difference here. The best-tested of the lot need not be well-tested!

2. Falsification: Sober made a point of saying that his account does not falsify models or hypotheses. We are to start out with all the possible models to be considered (hopefully including one that is true or approximately true), akin to the “closed universe” of standard Bayesian accounts[i], but do we not get rid of any as falsified, given data? It seems not.

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PhilStatLaw: “Let’s Require Health Claims to Be ‘Evidence Based'” (Schachtman)

I see that Nathan Schachtman has had many interesting posts during the time I was away.  His recent post endorses the idea of “a hierarchy of evidence”–but philosophers of “evidence-based” medicine generally question or oppose it, at least partly because of disagreement as to where to place RCTs in the hierarchy.  What do people think?

Litigation arising from the FDA’s refusal to approval “health claims” for foods and dietary supplements is a fertile area for disputes over the interpretation of statistical evidence.  A ‘‘health claim’’ is ‘‘any claim made on the label or in labeling of a food, including a dietary supplement, that expressly or by implication … characterizes the relationship of any substance to a disease or health-related condition.’’ 21 C.F.R. § 101.14(a)(1); see also 21 U.S.C. § 343(r)(1)(A)-(B).

Unlike the federal courts exercising their gatekeeping responsibility, the FDA has committed to pre-specified principles of interpretation and evaluation. By regulation, the FDA gives notice of standards for evaluating complex evidentiary displays for the ‘‘significant scientific agreement’’ required for approving a food or dietary supplement health claim.  21 C.F.R. § 101.14.  SeeFDA – Guidance for Industry: Evidence-Based Review System for the Scientific Evaluation of Health Claims – Final (2009).

If the FDA’s refusal to approve a health claim requires pre-specified criteria of evaluation, then we should be asking ourselves why have the federal courts failed to develop a set of criteria for evaluating health effects claims as part of its Rule 702 (“Daubert“) gatekeeping responsibilities.  Why, after close to 20 years after the Supreme Court decided Daubert, can lawyers make “health claims” without having to satisfy evidence-based criteria?

Read the rest.

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Further Reflections on Simplicity: Mechanisms

To continue with some philosophical reflections on the papers from the “Ockham’s razor” conference, let me respond to something in Shalizi’s recent comments ( His emphasis on the interest in understanding processes and mechanisms, as opposed to mere prediction, seems exactly right. But he raises a question that seems to me simply answered (on grounds of evidence):  If “a model didn’t seem to need” a mechanism, it is left out, why?

“It’s this, the leave-out-processes-you-don’t-need, which seems to me the core of the Razor for scientific model-building. This is definitely not the same as parameter-counting, and I think it’s also different from capacity control and even from description-length-measuring (cf.), though I am open to Peter persuading me otherwise. I am not, however, altogether sure how to formalize it, or what would justify it, beyond an aesthetic preference for tidy models. (And who died and left the tidy-minded in charge?) The best hope for such justification, I think, is something like Kevin’s idea that the Razor helps us get to the truth faster, or at least with fewer needless detours. Positing processes and mechanisms which aren’t strictly called for to account for the phenomena is asking for trouble needlessly.”

But it is easy to see that if a model M is adequate for data x regarding an aspect of a phenomenon (i.e., M had passed reasonably severe tests with x) , then a model M’ that added an “unnecessary” mechanism would have passed with very low severity, or, if one prefers, M’ would be very poorly corroborated.  To justify “leaving-out-processes-you-don’t-need” then, the appeal is not to aesthetics or heuristics but to the severity or well-testedness of M and M’.

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Deviates, Sloths, and Exiles: Philosophical Remarks on the Ockham’s Razor Workshop*

Picking up the pieces…

My flight out of Pittsburgh has been cancelled, and as I may be stuck in the airport for some time, I will try to make a virtue of it by jotting down some of my promised reflections on the “simplicity and truth” conference at Carnegie Mellon (organized by Kevin Kelly). My remarks concern only the explicit philosophical connections drawn by (4 of) the seven non-philosophers who spoke. For more general remarks, see blogs of: Larry Wasserman (Normal Deviate) and Cosma Shalizi (Three-Toed Sloth). (The following, based on my notes and memory, may include errors/gaps, but I trust that my fellow bloggers and sloggers, will correct me.)

First to speak were Vladimir Vapnik and Vladimir Cherkassky, from the field of machine learning, a discipline I know of only formally. Vapnik, of the Vapnik Chervonenkis (VC) theory, is known for his seminal work here. Their papers, both of which addressed directly the philosophical implications of their work, share enough themes to merit being taken up together.

Vapnik and Cherkassky find a number of striking dichotomies in the standard practice of both philosophy and statistics. They contrast the “classical” conception of scientific knowledge as essentially rational with the more modern, “data-driven” empirical view:

The former depicts knowledge as objective, deterministic, rational. Ockham’s razor is a kind of synthetic a priori statement that warrants our rational intuitions as the foundation of truth with a capital T, as well as a naïve realism (we may rely on Cartesian “clear and distinct” ideas; God does not deceive; and so on). The latter empirical view, illustrated by machine learning, is enlightened. It settles for predictive successes and instrumentalism, views models as mental constructs (in here, not out there), and exhorts scientists to restrict themselves to problems deemed “well posed” by machine-learning criteria.

But why suppose the choice is between assuming “a single best (true) theory or model” and the extreme empiricism of their instrumental machine learner? Continue reading

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CMU Workshop on Foundations for Ockham’s Razor

CMU Workshop on Foundations for Ockham’s Razor

Carnegie Mellon University, Center for Formal Epistemology:

Workshop on Foundations for Ockham’s Razor

All are welcome to attend.

June 22-24, 2012

Adamson WingBaker Hall 136A, Carnegie Mellon University

Workshop web page and schedule

Contact:  Kevin T. Kelly (

Rationale:  Scientific theory choice is guided by judgments of simplicity, a bias frequently referred to as “Ockham’s Razor”. But what is simplicity and how, if at all, does it help science find the truth? Should we view simple theories as means for obtaining accurate predictions, as classical statisticians recommend? Or should we believe the theories themselves, as Bayesian methods seem to justify? The aim of this workshop is to re-examine the foundations of Ockham’s razor, with a firm focus on the connections, if any, between simplicity and truth.


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Review of Error and Inference by C. Hennig

Theoria just sent me this review by Hennig* of Error and Inference.
in THEORIA 74 (2012): 245-247,

(Open access)

Deborah G. Mayo and Aris Spanos, eds. 2009. Error and Inference. Cambridge: Cambridge University Press.

Error and Inference focuses on the error-statistical philosophy of science (ESP) put forward by Deborah Mayo and Aris Spanos (MS). Chapters 1, 6 and 7 are mainly written by MS (partly with the statistician David Cox), whereas Chapters 2-5, 8, and 9 are driven by the contributions of other authors. There are responses to all these contributions at the end of the chapters, usually written by Mayo.

The structure of the book with the responses at the end of each chapter is a striking feature. The critical contributions enable a very lively discussion of ESP. On the other hand always having the last word puts Mayo and Spanos in a quite advantageous position. Some of the contributors may have underestimated Mayo’s ability to make the most of this advantage.

Central to ESP are the issues of probing scientific theories objectively by data, and Mayo’s concept of “severe testing” (ST). ST is based on a frequentist interpretation of probability, on conventional hypothesis testing and the associated error probabilities. ESP advertises a “piecemeal” approach to testing a scientific theory, in which various different aspects, which can be used to make predictions about data, are subjected to hypothesis tests. A statistical problem with such an approach is that failure of rejection of a null hypothesis H0 does not necessarily constitute evidence in favour of H0. The space of probability models is so rich that it is impossible to rule out all other probability models.

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An Error-Statistical Philosophy of Evidence (PH500, LSE Seminar)

This short paper, together with the response to comments by Casella and McCoy, may provide an OK overview of some issues/ideas, and as I’m making it available for my upcoming PH500 seminar*, I thought I’d post it too. The paper itself was a 15-minute presentation at the Ecological Society of America in 1998; my response to criticisms, around the same length, was requested much later. While in some ways the time lag shows, e.g., McCoy’s reference to “reductionist” accounts–part of the popular constructive leanings of the time; scant mention of Bayesian developments taking place around then, it is simple and short and non-technical **. Also, as I should hope, my own views have gone considerably beyond what I wrote then.

(Taper and Lele did an excellent job with this volume, as long as it took, particularly interspersing the commentary. I recommend it!***)

Mayo, D. (2004). “An Error-Statistical Philosophy of Evidence” in M. Taper and S. Lele (eds.) The Nature of Scientific Evidence: Statistical, Philosophical and Empirical Considerations. Chicago: University of Chicago Press: 79-118 (with discussion). Continue reading

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LSE Summer Seminar: Contemporary Problems in Philosophy of Statistics

As a visitor of the Centre for Philosophy of Natural and Social Science (CPNSS) at the London School of Economics and Political Science, I am planning to lead 5 seminars in the department of Philosophy, Logic, and Scientific Method this summer (2) and autumn (3) on Contemporary Philosophy of Statistics under the PH500 rubric, (listed under summer term).   This will be rather informal, based on the book I am writing with this name. There will be at least one guest seminar leader in the fall. Anyone interested in attending or finding out more may write to me: .*

Wednesday   6th June            3-5pm                        T206

Wednesday 13th June             3-5pm                        T206

Autumn term dates: To Be Announced

LSE contact

PH 500. Contemporary Problems in Philosophy of Statistical Science Continue reading

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Comedy Hour at the Bayesian (Epistemology) Retreat: Highly Probable vs Highly Probed

Bayesian philosophers (among others) have analogous versions of the criticism in my April 28 blogpost: error probabilities (associated with inferences to hypotheses) may conflict with chosen posterior probabilities in hypotheses. Since it’s Saturday night let’s listen in to one of the comedy hours at the Bayesian retreat (note the sedate philosopher’s comedy club backdrop):

Did you hear the one about the frequentist error statistical tester who inferred a hypothesis H passed a stringent test (with data x)?

The problem was, the epistemic probability in H was so low that H couldn’t be believed!  Instead we believe its denial H’!  So, she will infer hypotheses that are simply unbelievable!

So clearly the error statistical testing account fails to serve in an account of knowledge or inference (i.e., an epistemic account). However severely I might wish to say that a hypothesis H has passed a test, the Bayesian critic assigns a sufficiently low prior probability to H so as to yield a low posterior probability in H[i].  But this is no argument about why this counts in favor of, rather than against, their Bayesian computation as an appropriate assessment of the warrant to be accorded to hypothesis H.

To begin with, in order to use techniques for assigning frequentist probabilities to events, their examples invariably involve “hypotheses” that consist of asserting that a sample possesses a characteristic, such as “having a disease” or “being college ready” or, for that matter, “being true.”  This would not necessarily be problematic if it were not for the fact that their criticism requires shifting the probability to the particular sample selected—for example, a student Isaac is college-ready, or this null hypothesis (selected from a pool of nulls) is true.  This was, recall, the fallacious probability assignment that we saw in Berger’s attempt, later (perhaps) disavowed. Also there are just two outcomes, say s and ~s, and no degrees of discrepancy from H. Continue reading

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Jean Miller: Happy Sweet 16 to EGEK #2 (Hasok Chang Review of EGEK)

Jean Miller here, reporting back from the island. Tonight we complete our “sweet sixteen” celebration of Mayo’s EGEK (1996) with the book review by Dr. Hasok Chang (currently the Hans Rausing Professor of History and Philosophy of Science at the University of Cambridge). His was chosen as our top favorite in the category of ‘reviews by philosophers’. Enjoy!

REVIEW: British Journal of the Philosophy of Science 48 (1997), 455-459
DEBORAH MAYO Error and the Growth of Experimental Knowledge, 
The University of Chicago Press, 1996
By: Hasok Chang

Deborah Mayo’s Error and the Growth of Experimental Knowledge is a rich, useful, and accessible book. It is also a large volume which few people can realistically be expected to read cover to cover. Considering those factors, the main focus of this review will be on providing various potential readers with guidelines for making the best use of the book.

As the author herself advises, the main points can be grasped by reading the first and the last chapters. The real benefit, however, would only come from studying some of the intervening chapters closely. Below I will offer comments on several of the major strands that can be teased apart, though they are found rightly intertwined in the book. Continue reading

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Jean Miller: Happy Sweet 16 to EGEK! (Shalizi Review: “We Have Ways of Making You Talk”)

Jean Miller here.  (I obtained my PhD with D. Mayo in Phil/STS at VT.) Some of us “island philosophers” have been looking to pick our favorite book reviews of EGEK (Mayo 1996; Lakatos Prize 1999) to celebrate its “sweet sixteen” this month. This review, by Dr. Cosma Shalizi (CMU, Stat) has been chosen as the top favorite (in the category of reviews outside philosophy).  Below are some excerpts–it was hard to pick, as each paragraph held some new surprise, or unique way to succinctly nail down the views in EGEK. You can read the full review here. Enjoy.

“We Have Ways of Making You Talk, or, Long Live Peircism-Popperism-Neyman-Pearson Thought!”
by Cosma Shalizi

After I’d bungled teaching it enough times to have an idea of what I was doing, one of the first things students in my introductory physics classes learned (or anyway were taught), and which I kept hammering at all semester, was error analysis: estimating the uncertainty in measurements, propagating errors from measured quantities into calculated ones, and some very quick and dirty significance tests, tests for whether or not two numbers agree, within their associated margins of error. I did this for purely pragmatic reasons: it seemed like one of the most useful things we were supposed to teach, and also one of the few areas where what I did had any discernible effect on what they learnt. Now that I’ve read Mayo’s book, I’ll be able to offer another excuse to my students the next time I teach error analysis, namely, that it’s how science really works.

I exaggerate her conclusion slightly, but only slightly. Mayo is a dues-paying philosopher of science (literally, it seems), and like most of the breed these days is largely concerned with questions of method and justification, of “ampliative inference” (C. S. Peirce) or “non-demonstrative inference” (Bertrand Russell). Put bluntly and concretely: why, since neither can be deduced rigorously from unquestionable premises, should we put more trust in David Grinspoon‘s ideas about Venus than in those of Immanuel Velikovsky? A nice answer would be something like, “because good scientific theories are arrived at by employing thus-and-such a method, which infallibly leads to the truth, for the following self-evident reasons.” A nice answer, but not one which is seriously entertained by anyone these days, apart from some professors of sociology and literature moonlighting in the construction of straw men. In the real world, science is alas fallible, subject to constant correction, and very messy. Still, mess and all, we somehow or other come up with reliable, codified knowledge about the world, and it would be nice to know how the trick is turned: not only would it satisfy curiosity (“the most agreeable of all vices” — Nietzsche), and help silence such people as do, in fact, prefer Velikovsky to Grinspoon, but it might lead us to better ways of turning the trick. Asking scientists themselves is nearly useless: you’ll almost certainly just get a recital of whichever school of methodology we happened to blunder into in college, or impatience at asking silly questions and keeping us from the lab. If this vice is to be indulged in, someone other than scientists will have to do it: namely, the methodologists. Continue reading

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Earlier U-Phils and Deconstructions

Dear Reader: If you wish to see some previous rounds of philosophical analyses and deconstructions on this blog, we’ve listed some of them below:(search this blog under “U-Phil” for more)

Introductory explanation:

Mayo on Jim Berger:

Contributed deconstructions of J. Berger:

J. Berger on J. Berger:

Mayo on Senn:

Others on Senn:

Gelman on Senn:

Senn on Senn:

Mayo, Senn & Wasserman on Gelman:

Hennig on Gelman:

Deconstructing Dutch books:

Deconstructing Larry Wasserman

Aris Spanos on Larry Wasserman

Hennig and Gelman on Wasserman

Wasserman replies to Spanos and Hennig

concluding the deconstruction: Wasserman-Mayo


There are  others, but this should do; if you care to write on my previous post (send directly to


D Mayo

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