Onto-Meth conference

Several of the talks reflect a push-back against the idea that the determination of “ontology” in science—e.g., the objects and processes of theories, models and hypotheses—is (or should strive to correspond to?)  “real” objects in the world and/or what is approximately the case about them. Instead, at least some of the speakers wish to liberate ontology to recognize how “merely” pragmatic goals, needs, and desires are not just second-class citizens, but can and do (and should?) determine the categories of reality. Well there are a dozen equivocations here, most of which we did not really discuss at the conference.

In my own half of the Spanos-Mayo (D & P presentation[4]) I granted and even promoted the idea of a methodology that was pragmatic while also objective, so I’m not objecting to that part. The measurement of my weight is a product of “discretionary” judgments (e.g., to weigh in pounds with a scale having a given precision), but it is also a product of how much I really weigh (no getting around it). By understanding the properties of methodological tools and measuring systems, it is possible to “subtract out” the influence of the judgments to get at what is actually the case. At least approximately. But that view is different, it seems to me, from someone like Larry Laudan (at least in his later metamorphosis). Even though he considers his “reticulated” view a fairly hard-nosed spin on the Kuhnian idea of scientific paradigms as invariably containing an ontology (e.g., theories), a methodology, and (what he called) an “axiology” or set of aims (OMA), Laudan seems to think standards are so variable that what counts as evidence is constantly fluctuating (aside from maybe retaining the goal of fitting diverse facts). So I wonder if these pragmatic leanings are more like Laudan or more like me (and my view here, I take it, is essentially that of Peirce). I am perfectly sympathetic to the piecemeal “locavoracity” idea in Ruesche, by the way.

My worry, one of them, is that all kinds of rival entities and processes arise to account for (accord with, predict, and purportedly explain) data and patterns in data, and don’t we need ways to discriminate them? During the open discussion, I mentioned several examples, some of which I can make out all scrunched up in the corners of my coffee-logged program, such as appeals to “cultural theories” of risk and risk perceptions. These theories say appeals to supposedly “real” hazards, e.g, chance of disease, death, catastrophe, and other “objective” risk assessments are wrong.  They say it is not only possible but preferable (truer?) to capture attitudes toward risks, e.g., GM foods, nuclear energy, climate change, breast implants, etc. by means of one or another favorite politico-cultural grid-group categories (e.g., marginal-individualists, passive-egalitarians, hierarchical-border people, fatalists,  etc.). (Your objections to these vague category schemes are often taken as further evidence that you belong in one of the pigeon-holes!) And the other day I heard a behavioral economist declare that he had found the “mechanism” to explain deciding between options in virtually all walks of life using a regression parameter, he called it beta, and guess what? beta = 1/3! He proved it worked statistically too. He might be right, he had a lot of data. Anyway, in my deliberate attempt to trigger discussion at the conference end, I was wondering if some of the speakers and/or attendees (Danks, Woodward, Glymour? Anyone?) had anything to say about cases that some of us might wish to call reification.

I am tempted to view Woodward’s idea of developing heuristics for choosing variables as well-captured by my favorite goal: finding things out via severe tests (be stringent but learn something, promote error correcting improvements, etc.) One can start almost anywhere, and with adequate error probes speed up the goals of finding things out (yet another Peircean theme). Woodward did not say whether the rationale behind his heuristics would be something along these lines. But a rational is needed, or so I would claim. I was trying (in the discussion) to drive home this felt need to articulate a rationale, without which I suspect one overlooks the creative drive toward satisfying these heuristics; I mean why prefer these heuristics? That they may be found satisfied in “successful science” (after the fact) would not necessarily mean they identified forward-looking rules or criteria.

Maybe it’s the contrarian in me, but I might like to add a heuristic such as:

• find ways to suspect your variables and model even though all the previous heuristic rules are well-satisfied.

Or,

• pursue variables that fail to satisfy your preference for “variables that have unambiguous effects under manipulation”—as of now, given all we know–and discover a novel way to discriminate them anyway.

Or, another way to get at my contrarian inklings, suppose variables have been chosen along the lines of Woodward’s heuristics, and everything seems hunky dory. What impetus is there to find out how the model may be wrong (despite satisfying all those nice expectations)? Retrospectively, these rules might be satisfied, but prospectively, might not they encourage leaning back? (not to allude to the one year anniversary of Facebook’s IPO).

There are some other chicken scratchings I may come back to if I hear from anyone….

Gregory Gandenberger
Ph.D graduate student: Dept. of History and Philosophy of Science & Dept. of Statistics
University of Pittsburgh
http://gsganden.tumblr.com/

Onto-Meth conference

Some Thoughts on the O&M 2013 Conference
I was struck by how little speakers at the Ontology and Methodology conference engaged with the realism/antirealism debate. Laura Ruetsche defended a version of Arthur Fine’s Natural Ontological Attitude (NOA) in the first talk of the conference, but none of the speakers after her addressed the debate directly. David Danks and Jim Woodward made it particularly clear that they were deliberately avoiding questions about realism in favor of questions about what kinds of ontologies our theories should have in order to best serve the various purposes for which we develop them.

I am not criticizing the speakers! I am inclined to agree with Clark Glymour that the kinds of questions Danks and Woodward addressed are more interesting and important than questions about “what’s really real.” On the other hand, I worry that we lose something when we focus only on the use of science toward such ends as prediction and control. During the discussion period at the end of the conference, Peter Godfrey-Smith argued that science has some value simply for telling us what really is the case. For instance, science tells us that all living things on earth have a common ancestor, and that fact is a good thing to know regardless of whether or not it helps us predict or control anything.

One feature of the realism/antirealism debate that has long bothered me is that it treats all of “our best sciences” as if they had roughly the same epistemic status. In fact, realism about quantum field theory, for instance, is much harder to defend than realism about evolutionary biology. I am inclined to dismiss the realism debate as ill-formed insofar as it presumes that the question of scientific realism is a single question that spans all of the sciences. I am also suspicious of the debate in its bread-and-butter domain of fundamental physics. It is not clear to me that there is such a thing as fundamental physics; that if there is such a thing as fundamental physics, then it is converging toward a unified ontology; that if it is converging toward a unified ontology, then we can make sense of the question whether or not that ontology is correct; or that if we can make sense of the question whether or not that ontology is correct, then we have the means to give a justified answer to that question.

Nevertheless, as Glymour pointed out during the open discussion period, there are still good and open questions to address about whether and how we are justified in believing that science tells us the truth in other domains (such as evolutionary theory) where the realism question seems relatively well-formed and answerable. We can dismiss questions about “what’s really real” at a “fundamental level” while still thinking that philosophers of science should have a story to tell the 46% of Americans who believe that human beings were created in more or less their current form within the last 10,000 years—not a story about how science serves purposes of prediction and control, but a story about how science can help us find the truth.

2010 American Statistical Association and the American Society for Quality

TECHNOMETRICS, AUGUST 2010, VOL. 52, NO. 3, Book Reviews, 52:3, pp. 362-370.

Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, edited by Deborah G. MAYO and Aris SPANOS, New York: Cambridge University Press, 2010, ISBN 978-0-521-88008-4, xvii+419 pp., $60.00.

This edited volume contemplates the interests of both scientists and philosophers regarding gathering reliable information about the problem/question at hand in the presence of error, uncertainty, and with limited data information.

The volume makes a significant contribution in bridging the gap between scientific practice and the philosophy of science. The main contribution of this volume pertains to issues of error and inference, and showcases intriguing discussions on statistical testing and providing alternative strategy to Bayesian inference. In words, it provides cumulative information towards the philosophical and methodological issues of scientific inquiry at large.

The target audience of this volume is quite general and open to a broad readership. With some reasonable knowledge of probability theory and statistical science, one can get the maximum benefit from most of the chapters of the volume. The volume contains original and fascinating articles by eminent scholars (nine, including the editors) who range from names in statistical science to philosophy, including D. R. Cox, a name well known to statisticians.

The editors have done a superb job in presenting, organizing, and structuring the material in a logical order. The “Introduction and Background” is nicely presented and summarized, allowing for a smooth reading of the rest of the volume. There is a broad range of carefully selected topics from various related fields reflecting recent developments in these areas. The rest of the volume is divided in nine chapters/sections as follows:

1. Learning from Error, Severe Testing, and the Growth of Theoretical

Knowledge

2. The Life of Theory in the New Experimentalism

3. Revisiting Critical Rationalism

4. Theory Confirmation and Novel Evidence

5. Induction and Severe Testing

6. Theory Testing in Economics and the Error-Statistical Perspective

7. New Perspectives on (Some Old) Problems of Frequentist Statistics

8. Casual Modeling, Explanation and Severe Testing

9. Error and Legal Epistemology

In summary, this volume contains a wealth of knowledge and fascinating debates on a host of important and controversial topics equally important to the philosophy of science and scientific practice. This is a must-read—I enjoyed reading it and I am sure you will too! The book gives a sense of security regarding the future of statistical science and its importance in many walks of life. I also want to take the opportunity to suggest another seemingly related book by Harman and Kulkarni (2007). The review of this book was appeared in Technometricsin May 2008 (Ahmed 2008).

The following are chapters in E & I (2010) written by Mayo and/or Spanos, if you’re interested. If you produce a palindrome meeting the extremely simple requirements for May (by May 25 or so), you can win a free copy!

• Cox D. R. and Mayo. D. G. (2010). “Objectivity and Conditionality in Frequentist Inference” in Error and Inference:  276-304.
• Mayo, D. G. (2010). “An Error in the Argument from Conditionality and Sufficiency to the Likelihood Principle” in Error and Inference:  305-14.
• Mayo, D. (2010). “Error, Severe Testing, and the Growth of Theoretical Knowledge” in Error and Inference: 28-57.
• Mayo, D. (2010). “Can Scientific Theories be Warranted with Severity? Exchanges with Alan Chalmers” in Error and Inference: 73-87.
• Mayo, D. (2010). “Towards Progressive Critical Rationalism: Exchanges with Alan Musgrave” in Error and Inference: 115-124.
• Mayo, D. (2010). “An Ad Hoc Save of a Theory of Adhocness? Exchanges with John Worrall” in Error and Inference: 55-169.
• Mayo, D. (2010). “Sins of the Epistemic Probabilist: Exchanges with Peter Achinstein” in Error and Inference: 189-201.
• Mayo, D. (2010). “Explanation and Testing: Exchanges with Clark Glymour” in Error and Inference: 351-363.
• Mayo, D. (2010). “Error and the Law: Exchanges with Larry Laudan” in Error and Inference: 397-409.
• Mayo, D. G. and Cox, D. R. (2010). “Frequentist Statistics as a Theory of Inductive Inference” in Error and Inference: 247-275.
• Mayo, D. and Spanos, A. (2010). “Introduction and Background: Part I: Central Goals, Themes, and Questions; Part II The Error-Statistical Philosophy” in Error and Inference: 1-14, 15-27.

• Spanos, A. (2010). Theory Testing in Economics and the Error-Statistical Perspective in Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability and the Objectivity and Rationality of Science (D. Mayo and A. Spanos eds.), Cambridge: Cambridge University Press:  202-246.
• Spanos, A. (2010). On a New Philosophy of Frequentist Inference Exchanges with David Cox and Deborah G. Mayo in Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability and the Objectivity and Rationality of Science (D. Mayo and A. Spanos eds.), Cambridge: Cambridge University Press:  325-330.
• Spanos, A. (2010). Graphical Causal Modeling ad Error Statistics Exchanges with Clark Glymour in Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability and the Objectivity and Rationality of Science (D. Mayo and A. Spanos eds.), Cambridge: Cambridge University Press:  364-375.

May 4 (Saturday):

8:30-9:00: Pastries & Coffee (Continental Breakfast) outside of Pamplin 2030

MORNING SESSIONS:

9:00-9:15—Welcome talk
9:15-10:00 Ruetsche: “Method, Metaphysics, and Quantum Theory”
10:00-10:25: Discussion

10:25-10:40 coffee break

10:40-11:05 Shech, “Phase Transitions, Ontology and Earman’s Sound Principle”
11:05-11:20: Discussion

11:20-12:05 Godfrey-Smith, “Evolution and Agency: A Case Study in Ontology and Methodology”
12:05-12:30: Discussion

12:30-1:30 Box Lunch

AFTERNOON SESSIONS:

1:30-1:55: Clatterbuck, “Drift Beyond Wright-Fisher: The Predictive Inequivalency of Drift Models”
1:55-2:10: Discussion

2:10- 2:55: Mayo & Spanos, “Ontology & Methodology in Statistical Modeling”
2:55-3:20: Discussion

3:20-3:30 coffee break

3:30-3:55: Karaca, “The method of robustness analysis and the problem of data-selection at the ATLAS experiment”
3:55-4:10: Discussion

4:10 – 4:55 Patton, “Theory Assessment and Ontological Argument”
4:55-5:20: Discussion

5:20- 5:45 Marcellesi, “Counterfactuals and ‘nonmanipulable’ properties: When bad methodology leads to bad metaphysics (and vice-versa)”
5:45-6:00: Discussion

7:15–DINNER

May 5 (Sunday):

8:30-9:00: Pastries & Coffee (Continental Breakfast) outside of Pamplin 2030

MORNING SESSIONS:

9:00-9:45: Danks, “The Myriad Influences of Goals on Ontology”

9:45-10:10 Discussion

10:10-10:55 Hoover, “The Ontological Status of Shocks and Trends in Macroeconomics”
10:55-11:20: Discussion

11:20-11:35 Coffee break

11:35-12:00  Angner, “Behavioral vs. Neoclassical Economics: A Weberian Analysis”
12:00-12:15: Discussion

12:15-1:45 BREAK FOR LUNCH

AFTERNOON SESSIONS:

1:45-2:30 Woodward, “The Problem of Variable Choice”
2:30-2:55: Discussion

2:55-3:40 Jantzen, “The Algebraic Conception of Natural Kinds”
3:40-4:05: Discussion

4:05-5:15 Overview: Open Discussion & libations

Marilyn Monroe not walking past a Higgs boson and not making it decay, whatever philosophers might say.

My colleague, Lydia Patton, sent me this interesting article, “The Philosophy of the Higgs,” (from The Guardian, March 24, 2013) when I began the posts on “statistical flukes” in relation to the Higgs experiments (here and here); I held off posting it partly because of the slightly sexist attention-getter pic  of Marilyn (in reference to an “irrelevant blonde”[1]), and I was going to replace it, but with what?  All the men I regard as good-looking have dark hair (or no hair). But I wanted to take up something in the article around now, so here it is, a bit dimmed. Anyway apparently MM was not the idea of the author, particle physicist Michael Krämer, but rather a group of philosophers at a meeting discussing philosophy of science and science. In the article, Krämer tells us:

For quite some time now, I have collaborated on an interdisciplinary project which explores various philosophical, historical and sociological aspects of particle physics at the Large Hadron Collider (LHC). For me it has always been evident that science profits from a critical assessment of its methods. “What is knowledge?”, and “How is it acquired?” are philosophical questions that matter for science. The relationship between experiment and theory (what impact does theoretical prejudice have on empirical findings?) or the role of models (how can we assess the uncertainty of a simplified representation of reality?) are scientific issues, but also issues from the foundation of philosophy of science. In that sense they are equally important for both fields, and philosophy may add a wider and critical perspective to the scientific discussion. And while not every particle physicist may be concerned with the ontological question of whether particles or fields are the more fundamental objects, our research practice is shaped by philosophical concepts. We do, for example, demand that a physical theory can be tested experimentally and thereby falsified, a criterion that has been emphasized by the philosopher Karl Popper already in 1934. The Higgs mechanism can be falsified, because it predicts how Higgs particles are produced and how they can be detected at the Large Hadron Collider.

On the other hand, some philosophers tell us that falsification is strictly speaking not possible: What if a Higgs property does not agree with the standard theory of particle physics? How do we know it is not influenced by some unknown and thus unaccounted factor, like a mysterious blonde walking past the LHC experiments and triggering the Higgs to decay? (This was an actual argument given in the meeting!) Many interesting aspects of falsification have been discussed in the philosophical literature. “Mysterious blonde”-type arguments, however, are philosophical quibbles and irrelevant for scientific practice, and they may contribute to the fact that scientists do not listen to philosophers.

I entirely agree that philosophers have wasted a good deal of energy maintaining that it is impossible to solve Duhemian problems of where to lay the blame for anomalies. They misrepresent the very problem by supposing there is a need to string together a tremendously long conjunction consisting of a hypothesis H and a bunch of auxiliaries A_i which are presumed to entail observation e. But neither scientists nor ordinary people would go about things in this manner. The mere ability to distinguish the effects of different sources suffices to pinpoint blame for an anomaly. For some posts on falsification, see here and here*.

The question of why scientists do not listen to philosophers was also a central theme of the recent inaugural conference of the German Society for Philosophy of Science. I attended the conference to present some of the results of our interdisciplinary research group on the philosophy of the Higgs. I found the meeting very exciting and enjoyable, but was also surprised by the amount of critical self-reflection.

In the opening talk Peter Godfrey-Smith from the City University of New York emphasized three roles for philosophy: an integrative role, whereby philosophy can assess and connect various fields with an emphasis on generic categories and perspectives; an incubator role, where philosophy develops new ideas in a broad and speculative form, which are then pursued in a more focussed and specific way within an individual science; and an educative role, where philosophy teaches various general skills, including critical and abstract thinking. The problem I see with the integrative and incubator roles of philosophy is the high degree of specialization in modern science. It is very hard for a philosopher to keep up with scientific progress, and how could one integrate various fields without having fully appreciated the essential features of the individual sciences? As Margaret Morrison from the University of Toronto pointed out in her talk, if philosophy steps back too far from the individual sciences, the account becomes too general and isolated from scientific practice. On the other hand, if philosophy is too close to an individual science, it may not be philosophy any longer.

I think philosophy of science should not consider itself primarily as a service to science, but rather identify and answer questions within its own domain. I certainly would not be concerned if my own research went unnoticed by biologists, chemists, or philosophers, as long as it advances particle physics. On the other hand, as Morrison pointed out, science does generate its own philosophical problems, and philosophy may provide some kind of broader perspective for understanding those problems.

So then, should we physicists listen to philosophers?

An emphatic “No!”, if philosophers want to impose their preconceptions of how science should be done. I do not subscribe to Feyerabend’s provocative claim that “anything goes” in science, but I believe that many things go, and certainly many things should be tried.

But then, “Yes!”, we should listen, as philosophy can provide a critical assessment of our methods, in particular if we consider physics to be more than predicting numbers and collecting data, but rather an attempt to understand and explain the world. And even if philosophy might be of no direct help to science, it may be of help to scientists through its educational role, and sharpen our awareness of conceptional problems in our research**.

What I want to talk about are the roles of philosophers of science. While I do not disagree with the roles Godfrey-Smith allots philosophers of science, to incubate, integrate, and educate (about things like logic and critical thinking), and his list would not preclude what I have in mind, I would press to go much further. To focus just on one of my own areas of interest, there is enormous unclarity in discussions by statistical practitioners regarding such philosophical notions as objectivity, truth, falsifiability, evidence, inductive inference, and the roles of probability in modeling and inference. It is as if a certain trepidation and groupthink take over when it comes to philosophically tinged notions, and philosophers are rarely consulted to lend insight.  When they are, I’m afraid, they do not escape the criticism Stephen Weinberg raises in the linked Godfrey-Smith article (i.e., being wedded to a position that grows out of “theory-laden” philosophy, where the theories are philosophical.) Fresh methodological problems arise in practice, but philosophers of science are not consulted. Nor is it surprising. Peter Achinstein[2] has often said that scientists do not and should not consult philosophical accounts about evidence,because while scientists evaluate evidence empirically, philosophical accounts are merely based on a priori computations. Sad, if still true.

By and large, philosophers of science have reneged on the promise of the 80s to be relevant to science. In some areas, in particular the one I know best, philosophers of science have gone backwards. Philosophers of statistics were ahead of their time in the 70s and early 80s, engaging in discussions side by side with statistical practitioners (Godambe and Sprott 1971, Harper and Hooker, 1977 come to mind.) Contributions to the field were as likely to be by a philosopher as by a statistician. I talk about this much more elsewhere (e.g., the introduction to Mayo and Spanos, Error and Inference (CUP 2010), so I’m being quick here. Soon after I got my Ph.D, things seemed to dissipate…

Nowadays, while the foundations of statistics are being considered anew by many statisticians, philosophers of statistics are almost nowhere to be found.  Arguments given for some very popular slogans (mostly by non-philosophers), are too readily taken on faith as canon by others, and are repeated as gospel. Examples are easily found: all models are false, no models are falsifiable, everything is subjective, or equally subjective and objective, and the only properly epistemological use of probability is to supply posterior probabilities for quantifying actual or rational degrees of belief. Then there is the cluster of “howlers” allegedly committed by frequentist error statistical methods repeated verbatim (discussed on this blog). Margaret Morrison is right that many ask: is truly relevant philosophy really philosophy? I and a few others[3] think the answer is Yes! I have organized conferences[4] and published papers that address these issues, and it is the focus of a current book, nearing completion.

Even in the Higgs example, recall the controversy about whether particle physicists were misinterpreting their p-values; the letter-writing campaign by subjective Bayesians, etc. [5]. There is a valid question as to whether it is the philosopher of X’s responsibility to solve philosophical problems in domain X; and the answer will surely depend on the field. But in statistical science—itself sometimes regarded as “applied philosophy of science,” –I say the answer is, emphatically, yes! Their failure to do so has left them out of one of the most interesting periods in the areas of statistical science as well as machine learning.

*For a unit on Popper that includes Duhem’s problem and falsification, see http://errorstatistics.com/2012/02/01/no-pain-philosophy-skepticism-rationality-popper-and-all-that-part-2-duhems-problem-methodological-falsification/

[1] The article’s subtitle is: “Particle physicist Michael Krämer hangs out with philosophers and learns that one should be wary of irrelevant blondes” (whatever that means).

• Achinstein, P. (2001), The Book of Evidence, Oxford: Oxford University Press.
• Cox and Mayo 2010.

• Godambe, V.  and Sprott, D., (eds), (1971). Foundations of Statistical Inference, Holt, Rinehart and Winston of Canada, Toronto, 1971.

• Harper, W. L. and Hooker C. A. (eds.) (1976): Foundations of Probability Theory, Statistical Inference and Statistical Theories of Science. Vol. 2, Dordrecht, The Netherlands: D. Reidel

• Mayo, D. G. (2011) “Statistical Science and Philosophy of Science: Where Do/Should They Meet in 2011 (and beyond).” Rationality, Markets and Morals (RMM) 2, Special Topic: Statistical Science and Philosophy of Science, 79–102.

• Mayo, D. G. and Cox, D. R. (2011) “Statistical Scientist Meets a Philosopher of Science: A Conversation with Sir David Cox.”  Rationality, Markets and Morals (RMM), 2, Special Topic: Statistical Science and Philosophy of Science, 103-114.

• Mayo, D. G. and Spanos, A. (2010). “Introduction and Background: Part I: Central Goals, Themes, and Questions; Part II The Error-Statistical Philosophy” in Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability and the Objectivity and Rationality of Science (D Mayo and A. Spanos eds.), Cambridge: Cambridge University Press: 1-14, 15-27.

Three years ago, many of us were glued to the “spill cam” showing, in real time, the gushing oil from the April 20, 2010 explosion sinking the Deepwater Horizon oil rig in the Gulf of Mexico, killing 11, and spewing oil until July 15. Trials have been taking place this month, as people try to meet the 3 year deadline to sue BP and others. But what happened to the 200 million gallons of oil?  (Is anyone up to date on this?)  Has it vanished or just sunk to the bottom of the sea by dispersants which may have caused hidden destruction of sea life? I don’t know, but given it’s Saturday night around the 3 year anniversary, let’s listen into a reblog of a spill-related variation on the second of two original “overheard at the comedy hour” jokes. 

Error Statistics Philosophy Blog: March 2013* 

(3/1) capitalizing on chance
(3/4) Big Data or Pig Data?
(3/7) Stephen Senn: Casting Stones
(3/10) Blog Contents 2013 (Jan & Feb)
(3/11) S. Stanley Young: Scientific Integrity and Transparency
(3/13) Risk-Based Security: Knives and Axes
(3/15) Normal Deviate: Double Misunderstandings About p-values
(3/17) Update on Higgs data analysis: statistical flukes (1)
(3/21) Telling the public why the Higgs particle matters
(3/23) Is NASA suspending public education and outreach?
(3/27) Higgs analysis and statistical flukes (part 2)
(3/31) possible progress on the comedy hour circuit?

*March was incredibly busy here; I’m saving up several partially-baked posts on draft. Also, while I love this old typewriter, I’ve had to have special keys made for common statistical symbols, and that has delayed me some. I hope people will scan the previous contents starting from the beginning (e.g., with “prionvac“): it’s philosophy, remember, and philosophy has to be reread many times over.  January and February 2013 contents are here.

**compiled by Jean Miller and Nicole Jinn.

(To be placed in rejected posts blog)

 “They ought to let everything on that is sharp and pointy. Battle axes, machetes … you will not be able to take over the plane. It is as simple as that,” he said. (Link is here.)

I don’t have a strong feeling about blades, but I am very much in sync with the survey that influenced Pistole’s about face as regards cell phones (against) and liquids in carry-ons (for).

Vast majority of Americans say no to cell phone use and pocket knives inflight according to new survey

In a new, nationwide survey, Travel Leaders Group asked Americans across the country if they are in favor of the change and 73% of those polled do not want pocket knives allowed in airplane cabins. Also, a vast majority (nearly 80%) indicate they do not want fellow airline passengers to have the ability to make cell phone calls inflight. The survey includes responses from 1,788 consumers throughout the United States and was conducted by Travel Leaders Group – an $18 billion powerhouse in the travel industry – from March 15 to April 8, 2013.

“The results are very clear. Most Americans would prefer the status quo with regard to cell phone use inflight. Because so many planes are flying at near capacity and many passengers already feel a lack of personal space within the airplane cabin, it’s understandable that they want to continue to have some amount of peace and quiet whether they are on a short commuter flight or a flight that lasts several hours,” stated Travel Leaders Group CEO Barry Liben.

I’m really heartened to see that people are flouting the knee-jerk expectation that they’d want as much high tech as possible, and are weighing in against cell phones on planes. Recall my post on cell phones (now in rejected posts). Here are some of the statistics from the survey:

When asked, “Are you in favor of this change or against it?” 73% of those polled said they are not in favor of allowing pocket knives on planes.

I’m OK with it.	23.6%
I’m OK with everything except   pocket knives.	18.2%
I don’t think these items   should be allowed.	54.8%
I don’t know.	3.5%


Cell Phone Use Inflight

Studies are underway to determine if full cell phone use is safe while inflight and a decision on whether to allow such use (not just “airplane mode”) is expected this summer.  In Travel Leaders Group’s survey, nearly 80% of those polled are against allowing passengers to make cell phone calls during flight.  Here are the detailed responses:

I am opposed to it.	47.9%
I am in favor as long as it   is not used for conversations.	31.3%
I am in favor of it.	10.7%
I don’t know.	10.1%


Additional Statistics and Findings:

• Eliminate One TSA Security Measure: With regard to TSA security screening at the airport, when asked, “Which of the following TSA security measures would you most like to eliminate?” the top responses were: “removing of shoes” (27.9%), “limits on liquids in carry-on baggage” (24.1%), and “none, do not eliminate any security measures” (19.8%).

• Airport Security Satisfaction: When asked, “What is your level of satisfaction with airport security today?” 82.0% indicate they are satisfied or neutral with today’s security measures (62.2% indicate they are “satisfied,”19.8% are “neither satisfied nor unsatisfied” and 18.0% are “unsatisfied”).

• Coach Class Flyers: When asked, “Do you ever fly in Coach Class?” over 94% of those polled said “Yes.” And of those who indicate they fly in Coach Class, when asked what makes flying in Coach most uncomfortable, the top responses were: “Lack of leg room” (49.5%); “seat size” (17.2%) and “pitch of the seat – person in front of me reclines too much” (15.0%).

• This is the fifth consecutive year for this travel survey.  American consumers were engaged predominantly through social media channels such as Facebook and Twitter, as well as through direct contact with travel clients for the following Travel Leaders Group companies: Nexion, Results! Travel, Travel Leaders, Tzell Travel Group and Vacation.com.  (www.travelleadersgroup.com)

 So a tiny bit of good news among the forced air traffic control reductions and FAA cuts that began yesterday: See

http://rejectedpostsofdmayo.com/2013/04/22/msc-kvetch-air-traffic-control-cuts/

Head of Competence Center for Methodology and Statistics (CCMS)

Applied statisticians tend to perform analyses on additive scales and additivity is an important aspect of an analysis to try to check. Consider survival analysis. The most important model used, the default in many cases, is the proportional hazards model introduced by David Cox in 1972[1] and sometimes referred to as Cox regression. In fact, from one point of view, analysis takes place on the log-hazard scale and so the model could equally be referred to by the rather clumsier title additive log-hazards model and there is quite a literature on how the proportionality (or equivalently, additivity) assumption can be checked.

Words have a definite power on the mind and you sometimes encounter the nonsensical claim that if the proportionality assumption does not apply you should consider a log-rank test instead. In fact, when testing the null hypothesis that two treatments are identical, neither the log-rank test nor the score test using the proportional hazards model require the assumption of proportionality: the assumption is trivially satisfied by the fact of two treatments being identical. Furthermore the log-rank test is just a special case of proportional hazards: the score test for a proportional hazards model without any covariates is the log-rank test. Finally, it is easy to produce examples where proportional hazards would apply in a model with covariates but not in the model without covariates but very difficult to produce the converse.

An objection often made regarding such models is that they are very difficult for physicians to understand. My reply is to ask what is preferable: a difficult truth or an easy lie? Ah yes, it is sometimes countered, but surely I agree on the importance of clinical relevance. It is surely far more useful to express the results of a proportional hazards analysis in clinically relevant terms that can be understood, such as difference in median length of survival or the difference in the event rate up to a particular census point (say one year after treatment).

A disturbing paper by Snapinn and Jiang[2] points to a problem, however, and to explain it I can do no better that cite the abstract:

The standard analysis of a time-to-event variable often involves the calculation of a hazard ratio based on a survival model such as Cox regression; however, many people consider such relative measures of effect to be poor expressions of clinical meaningfulness. Two absolute measures of effect are often used to assess clinical meaningfulness: (1) many disease areas frequently use the absolute difference in event rates (or its inverse, the number-needed-to-treat) and (2) oncology frequently uses the difference between the median survival times in the two groups. While both of these measures appear reasonable, they directly contradict each other. This paper describes the basic mathematics leading to the two measures and shows examples. The contradiction described here raises questions about the concept of clinical meaningfulness. (p2341)

To see the problem, consider the following. The more serious the disease, the less a given difference in the rate at which people die will impact on the time survived and hence on differences in median survival. However, generally, the higher the baseline mortality rate the greater the difference in survival at a given time point that will be conveyed by a given treatment benefit.

If you find this less than clear, you have my sympathy. The only solution I can offer is to suggest that you read the paper by Snappin and Jiang[2]. However, in that case also consider the following point. If the point is so subtle, how many physicians who cannot understand proportional hazards can understand numbers needed to treat or differences in median survival? My opinion is that they can be counted on the fingers of one foot.

Let me explain the point at issue by analogy. If one were to study road traffic accidents one would find that among the very many factors affecting seriousness of the consequences of an accident would be the relative velocity at impact. However, if one looks at Newton’s laws of motion one finds that the second law speaks of the relationship between force, mass, and acceleration but not velocity. Now it is clear that a) acceleration being a concept that is defined (or at least understood) in terms of velocity (it is, indeed, derivative of velocity) it is a more complicated concept than velocity b) all cars have speedometers that show velocity but not acceleration c) the traffic laws are couched in terms of velocity rather than acceleration and d) it seems that from the point of view of human health it is velocity that is important.

None of this remotely constitutes an argument for replacing Newton’s second law. On the contrary what it implies is that you might need to work a little to use Newton’s laws to translate the effect of relative velocity into accident survivability. However, any attempt to simplify will run the danger of being an oversimplification.

This point was very well understood by a somewhat neglected scientist, the centenary of whose death falls this year:  James Berry( 1852-1913) an English executioner or hangman but one who recognised the value of physics. Ronald Meek, the Marxist economist whose work I was expected to study when a student of Economics and Statistics in the 1970s, devotes a chapter of his entertaining book, Figuring out Society[3], to Mr Berry. Berry decided that the length of the drop in an execution required scientific study: too short and death was not instantaneous, too long and decapitation ensued. He soon realised that a simple law of hanging, linear in the height of the drop, was wrong and instead came upon the idea of a ‘striking force’. This enabled him to hang criminals along a curve. (I understand that in American universities professors also sometimes execute judgement along a curve.)   The striking force required was adjusted according to the weight and neck musculature of the condemned and the height was then determined from the curve.

Many of the current proponents of evidence based medicine could learn from Berry’s example. NNTs derived from clinical trials are misleading indicators as to what will happen in clinical practice. For that to be the case would require that the patients in the clinical trial we run were a representative sample of the population of patients. They are not, and if they were the fact that we set such store on concurrent control would be inexplicable. To translate the results of clinical trials into practice may require a lot of work involving modelling and further background information. ‘Additive at the point of analysis but relevant at the point of application’ should be the motto.  Sometimes short cuts lead to long delays.

References

1.              Cox DR. Regression models and life-tables (with discussion). Journal of the Royal Statistical Society Series B 1972; 34: 187-220.

2.              Snapinn S, Jiang Q. On the clinical meaningfulness of a treatment’s effect on a time-to-event variable. Statistics in Medicine 2011; 30: 2341-2348.

3.              Meek RL. Figuring out society. Fontana, 1971.

Interestingly, I noticed the posts that have garnered the most comments have touched on philosophical questions of the nature of entities and processes behind statistical idealizations (e.g.,http://errorstatistics.com/2012/10/18/query/).

1. When an interpretation is supplied for a formal statistical account, its theorems may well turn out to express approximately true claims, and the interpretation may be deemed useful, but this does not mean the concepts give correct descriptions of reality. The interpreted axioms, and inference principles, are chosen to reflect a given philosophy, or set of intended aims: roughly, to use probabilistic ideas (i) to control error probabilities of methods (Neyman-Pearson, Fisher), or (ii) to assign and update degrees of belief, actual or rational (Bayesian).  But this does not mean its adherents have to take seriously the realism of all the concepts generated. In fact ,we often (on this blog) see supporters of various stripes of frequentist and Bayesian accounts running far away from taking their accounts literally, even as those interpretations are, or at least were, the basis and motivation for the development of the formal edifice (“we never meant this literally”).  But are these caveats on the same order? Or do some threaten the entire edifice of the account?

Starting with the error statistical account, recall Egon Pearson in his “Statistical Concepts in Their Relation to Reality” making it clear to Fisher that the business of controlling erroneous actions in the long run, acceptance sampling in industry and 5-year plans, only arose with Wald, and were never really part of the original Neyman-Pearson tests (declaring that the behaviorist philosophy was Neyman’s, not his).  The paper itself may be found here. I was interested to hear (Mayo 2005)  Neyman’s arch opponent, Bruno de Finetti, remark (quite correctly) that the expression “inductive behavior…that was for Neyman simply a slogan underlining and explaining the difference between his, the Bayesian and the Fisherian formulations” became with Abraham Wald’s work, “something much more substantial” (de Finetti 1972, 176).

Granted, it has not been obvious to people just how to interpret N-P tests “evidentially “ or “inferentially”—the subject of my work over many years. But there always seemed to me to be enough hints and examples to see what was intended: A statistical hypothesis H assigns probabilities to possible outcomes, and the warrant for accepting H as adequate—for an error statistician– is in terms of how well corroborated H is: how well H has stood up to tests that would have detected flaws in H, at least with very high probability. So the grounds for holding or using H are error statistical. The control and assessment of error probabilities may be used inferentially to determine the capabilities of methods to detect the adequacy/inadequacy of models, and express the extent of the discrepancies that have been identified. We also employ these ideas to detect gambits that make it too easy to find evidence for claims, even if the claims have been subjected to weak tests and biased procedures. A recent post is here.

The account has never professed to supply a unified logic, or any kind of logic for inference. The idea that there was a single rational way to make inferences was ridiculed by Neyman (whose birthday is April 16).

2. Proposed (“we never meant this literally”) withdrawals  from the Bayesian interpretations do not seem so innocuous. Perhaps some will say this just shows my bias. Let me grant that the popular idea of interpreting prior probability distributions as non-subjective, in some sense or other, is not so radical (though I’d still want to know how to interpret posteriors and why). But what we usually see now is some blurring of the two: touting the advantage of Bayesian methods because they incorporate background beliefs, while also advertising “conventional” (default, reference, or “objective”) priors as having minimal influence on inference. [1] See “Grace and amen Bayesianism within this deconstruction. Also relevant: Irony and Bad Faith: Deconstructing Bayesians.

Perhaps the most popular view nowadays regards the prior as some kind of uninterpreted mathematical construct, merely serving to get a posterior. These same Bayesians, some of them, advocate “testing” the prior, but this is hard to grasp if we do not know what the priors intend to be, or stand for.  Then there are those Bayesians, perhaps they are a radical (but influential) subgroup, who deny the machine of updating by Bayes theorem altogether.  In Gelman (2011) (our special topic of RMM):

“Our key departure from the mainstream Bayesian view (as expressed, for example, [in Wikipedia]) is that we do not attempt to assign posterior probabilities to models or to select or average over them using posterior probabilities. Instead, we use predictive checks to compare models to data and use the information thus learned about anomalies to motivate model improvements.” (p. 71).

In Gelman and Robert (2013), we hear that a major source of Bayesian criticism comes from assuming “that Bayesians actually seem to believe their assumptions rather than merely treating them as counters in a mathematical game.” (p. 3) This comes as a surprise to those of us who thought the Bayesians really meant it. So what is the game being played?

[W]e make strong assumptions and use subjective knowledge in order to make inferences and predictions that can be tested by comparing to observed and new data (see Gelman and Shalizi, 2012, or Mayo, 1996 for a similar attitude coming from a non-Bayesian direction). (p. 3)

So maybe some kind of a “non-Bayesian checking of Bayesian models” would offer more a more promising foundation, at least for Gelman’s brand of “Bayesian falsificationism” (Gelman 2011). See my 2013 Comments on Gelman and Shalizi [2]. On the face of it, any inference, whether to the adequacy of a model (for a given purpose), or to a posterior probability, can be said to be warranted just to the extent that the inference has withstood severe testing: one with a high probability of having found flaws were they present.  The ontology matters less than the epistemology.

Thus, the severity idea, could conceivably illuminate what’s going on with Gelman’s model checking; I find the idea promising, but do not really know what he thinks.

But to pursue such an avenue still requires reckoning with a fundamental issue at the foundations of Bayesian method: the interpretation of and justification for the prior probability distribution. Error statisticians use idealizations, but they are tightly constrained by the need for error probabilities, in a statistical model, to approximate the actual ones, even if only hypothetical, or checked by simulation. We are modeling real processes, not knowledge of processes.

Gelman and Robert (2013) allow:

“that many Bayesians over the years have muddied the waters by describing parameters as random rather than fixed. Once again, for Bayesians as much as for any other statistician, parameters are (typically) fixed but unknown. It is the knowledge about these unknowns that Bayesians model as random” (p. 4).

Bayesians will …assign a probability distribution to a parameter that one could not possibly imagine to have been generated by a random process, parameters such as the coefficient of party identification in a regression on vote choice, or the overdispersion in a network model, or Hubble’s constant in cosmology. There is no inconsistency in this opposition once one realizes that priors are not reflections of a hidden “truth” but rather evaluations of the modeler’s uncertainty about the parameter. (p. 3)

The choice, of course, is not between modeling a “hidden ‘truth’” and modeling “the modeler’s uncertainty”. Actually, in the majority of the examples I have seen, it seems better to imagine the parameter being generated by a random process.  On the other hand, “the modeler’s uncertainty about the parameter” is one of the most unclear parts of Bayesian modeling. It is not that we can’t see measuring the degree of evidence, corroboration, severity of test, or the like, that is accorded a claim about a fixed parameter.  We can and do. It is just that those measures will not be well represented as posterior or prior probabilities, obeying the probability calculus.

Possibly an idea I once proposed–a variation on a view held by the frequentist Reichenbach– can work (in EGEK, ch. 4 1996). Reichenbach suggested that scientists might eventually be able to assess the relative frequency with which a given type of hypothesis or theory is true. This might provide it a frequentist probability assignment. I don’t see how one could get such a relative frequency (or rather I can see many different reference sets that could be used), nor why knowing such quantities would be useful in appraising the evidence for a given hypothesis H. My variation (Chapter 4 Duhem, Kuhn, and Bayes, pp 120-4) is to consider the relative frequency with which evidence of a certain strength, (e.g., passing k tests with increasingly impressive error probabilities)  is generated, despite H being false. This is attainable. But that of course take us to an error probabilistic assessment!

Maybe this style of Bayesianism doesn’t need a clear ontology so long as it’s got a clear epistemology. But does it?***

What do readers think?

*To see the full list of speakers: “Ontology and Methodology” conference. Actually our presentation will likely take a different tack, but I still want to hear your thoughts.

**Registration is free, but required, by April 20-25.

***I should say right off (for those who do not know) that my work is not in metaphysics, but on philosophical problems about inductive-statisical inference , experiment and evidence.My colleague (and co-conference organizer) Ben Jantzen is the “ontology” guy, and the third colleague involved, Lydia Patton, does O & M as well as HPS.

For further references, see those within posts and papers linked here, or search this blog.

De Finetti, B. (1972), Probability, Induction, and Statistics: The Art of Guessing. NY, Wiley.

Gelman, A. (2011). Induction and deduction in Bayesian data analysis. Rationality, Markets and Morals (RMM) 2, 67–78.

Gelman, A.and C. Shalizi. (Article first published online: 24 Feb 2012). “Philosophy and the Practice of Bayesian statistics (with discussion)”.British Journal of Mathematical and Statistical Psychology (BJMSP).

Gelman, A, and Robert, C. (2013). Not only defended but also applied: The perceived absurdity of Bayesian inference.

http://www.stat.columbia.edu/~gelman/research/published/feller8.pdf

Kass and Wasserman, L. (1996). The Selection of Prior Distributions by Formal Rules. Journal of the American Statistical Association 91, 1343-1370.

Mayo, D. G. (1996).[EGEK] Error and the growth of experimental knowledge. Chicago: University of Chicago Press.

_____ (2005). Evidence as passing severe tests: Highly probable vs. highly probed hypotheses. In P. Achinstein (Ed.), Scientific Evidence (pp. 95-127). Baltimore: Johns Hopkins University Press.

_____ (2011). Statistical science and philosophy of science: where do/should they meet in 2011 (and beyond)?” Rationality, Markets and Morals (RMM) 2, Special Topic: Statistical Science and Philosophy of Science, 79–102.

_____ (2013). Comments on A. Gelman and C. Shalizi: Philosophy and the practice of Bayesian statistics. British Journal of Mathematical and Statistical Psychology, forthcoming.

Mayo, D. and Cox, D. (2010). Frequentist statistics as a theory of inductive inference. In D. Mayo and A. Spanos (Eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability and the Objectivity and Rationality of Science (pp. 247-275). Cambridge: Cambridge University Press. This paper appeared in The Second Erich L. Lehmann Symposium: Optimality, 2006, Lecture Notes-Monograph Series, Volume 49, Institute of Mathematical Statistics, 247-275.

Mayo, D. and Spanos, A. (2011). Error statistics. In P. Bandyopadhyay and M. Forster (Volume Eds.); D. M.Gabbay, P. Thagard and J. Woods (General Eds.). Philosophy of statistics: Handbook of philosophy of science Vol 7 (pp. 1-46). The Netherlands: Elsevier.

Pearson, E. S. (1955). Statistical concepts in their relation to reality.  Journal of the Royal Statistical SocietyB 17, 204-207.

Senn, S. (2011). You may believe you are a Bayesian but you are probably wrong. Rationality, Markets and Morals (RMM) 2, Special Topic: Statistical Science and Philosophy of Science, 48-66.

Koray Karaca
The method of robustness analysis and the problem of data-selection at the ATLAS experiment

In the first part, I characterize and distinguish between two problems of “methodological justification” that arise in the context of scientific experimentation. What I shall call the “problem of validation” concerns the accuracy and reliability of experimental procedures through which a particular set of experimental data is first acquired and later transformed into an experimental result. Therefore, the problem of validation can be phrased as follows: how to justify that a particular set of data as well as the procedures that transform it into an experimental result are accurate and reliable, so that the experimental result obtained at the end of the experiment can be taken as valid.  On the other hand, what I shall call the “problem of exploration” is concerned with the methodological question of whether an experiment is able, either or both, (1) to provide a genuine test of the conclusions of a scientific theory or hypothesis if the theory in question has not been previously (experimentally) tested, or to provide a novel test if the theory or hypothesis in question has already been tested, and (2) to discover completely novel phenomena; i.e., phenomena which have not been predicted by present theories and detected in previous theories. Even though the problem of validation and the ways it is dealt with in scientific practice has been thoroughly discussed in the literature of scientific experimentation, the significance of the problem of exploration has not yet been fully appreciated. In this work, I shall address this problem and examine the way it is handled in the present-day high collision-rate particle physics experiments. To this end, I shall consider the ATLAS experiment, which is one of the Large Hadron Collider (LHC) experiments currently running at CERN. …What are called “interesting events” are those collision events that are taken to serve to test the as-yet-untested predictions of the Standard Model of particle physics (SM) and its possible extensions, as well as to discover completely novel phenomena not predicted before by any theories or theoretical models.

To read the rest of the abstract, go to our just-made-public O & M conference blog.

First let me say that I’m delighted this case will be discussed at the O&M conference, and look forward to doing so. Here are a couple of reflections from the abstract, partly on terminology. First, I find it interesting that he places “tiggering” (what I alluded to in my last post as a behavioristic, pre-data, task) under “exploratory”. He may be focussed more on what occurs (in relation to this one episode anyhow) when data are later used to check for indications of anomalies for the Standard Model Higgs–having been “parked” for later analysis.  I thought the exploratory stage is usually a stage of informal or semi-formal data analysis to find interesting patterns and potential ingredients (variables, functions) for models, model building, and possible theory development.  When Strassler heard there would be “parked data” for probing anomalies, I take it his theories kicked in to program those exotic indicators. Second, it seems to me that philosophers of science and “confirmation theorists” of various sorts, have focussed on when “data,” all neat and tidied up, count as supporting, confirming, falsifying hypotheses and theories.  I wouldn’t have thought the problem of data collection, modeling or justifying data was “thoroughly discussed”–It absolutely should be– just that it seems all-too-rare. I may be wrong (I’d be glad to see references).

*Koray is a postdoctoral research fellow at the University of Wuppertal, and he knows I’m mentioning him here.