Error Statistics

Spot the power howler: α = ß?

Spot the fallacy!

  1. METABLOG QUERYThe power of a test is the probability of correctly rejecting the null hypothesis. Write it as 1 – β.
  2. So, the probability of incorrectly rejecting the null hypothesis is β.
  3. But the probability of incorrectly rejecting the null is α (the type 1 error probability).

So α = β.

I’ve actually seen this, and variants on it [i].

[1] Although they didn’t go so far as to reach the final, shocking, deduction.

 

Categories: Error Statistics, power, Statistics | 12 Comments

Higgs discovery three years on (Higgs analysis and statistical flukes)

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2015: The Large Hadron Collider (LHC) is back in collision mode in 2015[0]. There’s a 2015 update, a virtual display, and links from ATLAS, one of two detectors at (LHC)) here. The remainder is from one year ago. (2014) I’m reblogging a few of the Higgs posts at the anniversary of the 2012 discovery. (The first was in this post.) The following, was originally “Higgs Analysis and Statistical Flukes: part 2″ (from March, 2013).[1]

Some people say to me: “This kind of reasoning is fine for a ‘sexy science’ like high energy physics (HEP)”–as if their statistical inferences are radically different. But I maintain that this is the mode by which data are used in “uncertain” reasoning across the entire landscape of science and day-to-day learning (at least, when we’re trying to find things out)[2] Even with high level theories, the particular problems of learning from data are tackled piecemeal, in local inferences that afford error control. Granted, this statistical philosophy differs importantly from those that view the task as assigning comparative (or absolute) degrees-of-support/belief/plausibility to propositions, models, or theories. 

“Higgs Analysis and Statistical Flukes: part 2″images

Everyone was excited when the Higgs boson results were reported on July 4, 2012 indicating evidence for a Higgs-like particle based on a “5 sigma observed effect”. The observed effect refers to the number of excess events of a given type that are “observed” in comparison to the number (or proportion) that would be expected from background alone, and not due to a Higgs particle. This continues my earlier post (part 1). It is an outsider’s angle on one small aspect of the statistical inferences involved. But that, apart from being fascinated by it, is precisely why I have chosen to discuss it: we [philosophers of statistics] should be able to employ a general philosophy of inference to get an understanding of what is true about the controversial concepts we purport to illuminate, e.g., significance levels. Continue reading

Categories: Higgs, highly probable vs highly probed, P-values, Severity | Leave a comment

What Would Replication Research Under an Error Statistical Philosophy Be?

f1ce127a4cfe95c4f645f0cc98f04fcaAround a year ago on this blog I wrote:

“There are some ironic twists in the way psychology is dealing with its replication crisis that may well threaten even the most sincere efforts to put the field on firmer scientific footing”

That’s philosopher’s talk for “I see a rich source of problems that cry out for ministrations of philosophers of science and of statistics”. Yesterday, I began my talk at the Society for Philosophy and Psychology workshop on “Replication in the Sciences”with examples of two main philosophical tasks: to clarify concepts, and reveal inconsistencies, tensions and ironies surrounding methodological “discomforts” in scientific practice.

Example of a conceptual clarification 

Editors of a journal, Basic and Applied Social Psychology, announced they are banning statistical hypothesis testing because it is “invalid” (A puzzle about the latest “test ban”)

It’s invalid because it does not supply “the probability of the null hypothesis, given the finding” (the posterior probability of H0) (2015 Trafimow and Marks)

  • Since the methodology of testing explicitly rejects the mode of inference they don’t supply, it would be incorrect to claim the methods were invalid.
  • Simple conceptual job that philosophers are good at

(I don’t know if the group of eminent statisticians assigned to react to the “test ban” will bring up this point. I don’t think it includes any philosophers.)

____________________________________________________________________________________

 

Example of revealing inconsistencies and tensions 

Critic: It’s too easy to satisfy standard significance thresholds

You: Why do replicationists find it so hard to achieve significance thresholds?

Critic: Obviously the initial studies were guilty of p-hacking, cherry-picking, significance seeking, QRPs

You: So, the replication researchers want methods that pick up on and block these biasing selection effects.

Critic: Actually the “reforms” recommend methods where selection effects and data dredging make no difference.

________________________________________________________________

Whether this can be resolved or not is separate.

  • We are constantly hearing of how the “reward structure” leads to taking advantage of researcher flexibility
  • As philosophers, we can at least show how to hold their feet to the fire, and warn of the perils of accounts that bury the finagling

The philosopher is the curmudgeon (takes chutzpah!)

I also think it’s crucial for philosophers of science and statistics to show how to improve on and solve problems of methodology in scientific practice.

My slides are below; share comments.

Categories: Error Statistics, reproducibility, Statistics | 18 Comments

From our “Philosophy of Statistics” session: APS 2015 convention

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“The Philosophy of Statistics: Bayesianism, Frequentism and the Nature of Inference,” at the 2015 American Psychological Society (APS) Annual Convention in NYC, May 23, 2015:

 

D. Mayo: “Error Statistical Control: Forfeit at your Peril” 

 

S. Senn: “‘Repligate’: reproducibility in statistical studies. What does it mean and in what sense does it matter?”

 

A. Gelman: “The statistical crisis in science” (this is not his exact presentation, but he focussed on some of these slides)

 

For more details see this post.

Categories: Bayesian/frequentist, Error Statistics, P-values, reforming the reformers, reproducibility, S. Senn, Statistics | 10 Comments

“Error statistical modeling and inference: Where methodology meets ontology” A. Spanos and D. Mayo

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A new joint paper….

“Error statistical modeling and inference: Where methodology meets ontology”

Aris Spanos · Deborah G. Mayo

Abstract: In empirical modeling, an important desideratum for deeming theoretical entities and processes real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwine with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The key to untangling them is the realization that behind every substantive model there is a statistical model that pertains exclusively to the probabilistic assumptions imposed on the data. It is not that the methodology determines whether to be a realist about entities and processes in a substantive field. It is rather that the substantive and statistical models refer to different entities and processes, and therefore call for different criteria of adequacy.

Keywords: Error statistics · Statistical vs. substantive models · Statistical ontology · Misspecification testing · Replicability of inference · Statistical adequacy

To read the full paper: “Error statistical modeling and inference: Where methodology meets ontology.”

The related conference.

Mayo & Spanos spotlight

Reference: Spanos, A. & Mayo, D. G. (2015). “Error statistical modeling and inference: Where methodology meets ontology.” Synthese (online May 13, 2015), pp. 1-23.

Categories: Error Statistics, misspecification testing, O & M conference, reproducibility, Severity, Spanos | 2 Comments

Spurious Correlations: Death by getting tangled in bedsheets and the consumption of cheese! (Aris Spanos)

Spanos

Spanos

These days, there are so many dubious assertions about alleged correlations between two variables that an entire website: Spurious Correlation (Tyler Vigen) is devoted to exposing (and creating*) them! A classic problem is that the means of variables X and Y may both be trending in the order data are observed, invalidating the assumption that their means are constant. In my initial study with Aris Spanos on misspecification testing, the X and Y means were trending in much the same way I imagine a lot of the examples on this site are––like the one on the number of people who die by becoming tangled in their bedsheets and the per capita consumption of cheese in the U.S.

The annual data for 2000-2009 are: xt: per capita consumption of cheese (U.S.) : x = (29.8, 30.1, 30.5, 30.6, 31.3, 31.7, 32.6, 33.1, 32.7, 32.8); yt: Number of people who died by becoming tangled in their bedsheets: y = (327, 456, 509, 497, 596, 573, 661, 741, 809, 717)

I asked Aris Spanos to have a look, and it took him no time to identify the main problem. He was good enough to write up a short note which I’ve pasted as slides.

spurious-correlation-updated-4-1024

Aris Spanos

Wilson E. Schmidt Professor of Economics
Department of Economics, Virginia Tech

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*The site says that the server attempts to generate a new correlation every 60 seconds.

Categories: misspecification testing, Spanos, Statistics, Testing Assumptions | 14 Comments

Neyman: Distinguishing tests of statistical hypotheses and tests of significance might have been a lapse of someone’s pen

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Neyman, drawn by ?

Tests of Statistical Hypotheses and Their Use in Studies of Natural Phenomena” by Jerzy Neyman

ABSTRACT. Contrary to ideas suggested by the title of the conference at which the present paper was presented, the author is not aware of a conceptual difference between a “test of a statistical hypothesis” and a “test of significance” and uses these terms interchangeably. A study of any serious substantive problem involves a sequence of incidents at which one is forced to pause and consider what to do next. In an effort to reduce the frequency of misdirected activities one uses statistical tests. The procedure is illustrated on two examples: (i) Le Cam’s (and associates’) study of immunotherapy of cancer and (ii) a socio-economic experiment relating to low-income homeownership problems.

I hadn’t posted this paper of Neyman’s before, so here’s something for your weekend reading:  “Tests of Statistical Hypotheses and Their Use in Studies of Natural Phenomena.”  I recommend, especially, the example on home ownership. Here are two snippets:

1. INTRODUCTION

The title of the present session involves an element that appears mysterious to me. This element is the apparent distinction between tests of statistical hypotheses, on the one hand, and tests of significance, on the other. If this is not a lapse of someone’s pen, then I hope to learn the conceptual distinction. Continue reading

Categories: Error Statistics, Neyman, Statistics | Tags: | 18 Comments

Heads I win, tails you lose? Meehl and many Popperians get this wrong (about severe tests)!

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bending of starlight.

[T]he impressive thing about the 1919 tests of Einstein ‘s theory of gravity] is the risk involved in a prediction of this kind. If observation shows that the predicted effect is definitely absent, then the theory is simply refuted. The theory is incompatible with certain possible results of observation—in fact with results which everybody before Einstein would have expected. This is quite different from the situation I have previously described, [where]..it was practically impossible to describe any human behavior that might not be claimed to be a verification of these [psychological] theories.” (Popper, CR, [p. 36))

 

Popper lauds Einstein’s General Theory of Relativity (GTR) as sticking its neck out, bravely being ready to admit its falsity were the deflection effect not found. The truth is that even if no deflection effect had been found in the 1919 experiments it would have been blamed on the sheer difficulty in discerning so small an effect (the results that were found were quite imprecise.) This would have been entirely correct! Yet many Popperians, perhaps Popper himself, get this wrong.[i] Listen to Popperian Paul Meehl (with whom I generally agree).

The stipulation beforehand that one will be pleased about substantive theory T when the numerical results come out as forecast, but will not necessarily abandon it when they do not, seems on the face of it to be about as blatant a violation of the Popperian commandment as you could commit. For the investigator, in a way, is doing…what astrologers and Marxists and psychoanalysts allegedly do, playing heads I win, tails you lose.” (Meehl 1978, 821)

No, there is a confusion of logic. A successful result may rightly be taken as evidence for a real effect H, even though failing to find the effect need not be taken to refute the effect, or even as evidence as against H. This makes perfect sense if one keeps in mind that a test might have had little chance to detect the effect, even if it existed. The point really reflects the asymmetry of falsification and corroboration. Popperian Alan Chalmers wrote an appendix to a chapter of his book, What is this Thing Called Science? (1999)(which at first had criticized severity for this) once I made my case. [i] Continue reading

Categories: fallacy of non-significance, philosophy of science, Popper, Severity, Statistics | Tags: | 2 Comments

All I want for Chrismukkah is that critics & “reformers” quit howlers of testing (after 3 yrs of blogging)! So here’s Aris Spanos “Tallking Back!”

spanos 2014

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This was initially posted as slides from our joint Spring 2014 seminar: “Talking Back to the Critics Using Error Statistics”. (You can enlarge them.) Related reading is Mayo and Spanos (2011)

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Categories: Error Statistics, fallacy of rejection, Phil6334, reforming the reformers, Statistics | 27 Comments

“Probing with Severity: Beyond Bayesian Probabilism and Frequentist Performance” (Dec 3 Seminar slides)

(May 4) 7 Deborah Mayo  “Ontology & Methodology in Statistical Modeling”Below are the slides from my Rutgers seminar for the Department of Statistics and Biostatistics yesterday, since some people have been asking me for them. The abstract is here. I don’t know how explanatory a bare outline like this can be, but I’d be glad to try and answer questions[i]. I am impressed at how interested in foundational matters I found the statisticians (both faculty and students) to be. (There were even a few philosophers in attendance.) It was especially interesting to explore, prior to the seminar, possible connections between severity assessments and confidence distributions, where the latter are along the lines of Min-ge Xie (some recent papers of his may be found here.)

“Probing with Severity: Beyond Bayesian Probabilism and Frequentist Performance”

[i]They had requested a general overview of some issues in philosophical foundations of statistics. Much of this will be familiar to readers of this blog.

 

 

Categories: Bayesian/frequentist, Error Statistics, Statistics | 11 Comments

The Amazing Randi’s Million Dollar Challenge

09randi3-master675-v2-1The NY Times Magazine had a feature on the Amazing Randi yesterday, “The Unbelievable Skepticism of the Amazing Randi.” It described one of the contestants in Randi’s most recent Million Dollar Challenge, Fei Wang:

“[Wang] claimed to have a peculiar talent: from his right hand, he could transmit a mysterious force a distance of three feet, unhindered by wood, metal, plastic or cardboard. The energy, he said, could be felt by others as heat, pressure, magnetism or simply “an indescribable change.” Tonight, if he could demonstrate the existence of his ability under scientific test conditions, he stood to win $1 million.”

Isn’t “an indescribable change” rather vague?

…..The Challenge organizers had spent weeks negotiating with Wang and fine-tuning the protocol for the evening’s test. A succession of nine blindfolded subjects would come onstage and place their hands in a cardboard box. From behind a curtain, Wang would transmit his energy into the box. If the subjects could successfully detect Wang’s energy on eight out of nine occasions, the trial would confirm Wang’s psychic power. …”

After two women failed to detect the “mystic force” the M.C. announced the contest was over.

“With two failures in a row, it was impossible for Wang to succeed. The Million Dollar Challenge was already over.”

You think they might have given him another chance or something.

“Stepping out from behind the curtain, Wang stood center stage, wearing an expression of numb shock, like a toddler who has just dropped his ice cream in the sand. He was at a loss to explain what had gone wrong; his tests with a paranormal society in Boston had all succeeded. Nothing could convince him that he didn’t possess supernatural powers. ‘This energy is mysterious,’ he told the audience. ‘It is not God.’ He said he would be back in a year, to try again.”

The article is here. If you don’t know who A. Randi is, you should read it.

Randi, much better known during Uri Geller spoon-bending days, has long been the guru to skeptics and fraudbusters, but also a hero to some critical psi believers like I.J. Good. Geller continually sued Randi for calling him a fraud. As such, I.J. Good warned me that I might be taking a risk in my use of “gellerization” in EGEK (1996), but I guess Geller doesn’t read philosophy of science. A post on “Statistics and ESP Research” and Diaconis is here.

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I’d love to have seen Randi break out of these chains!

 

Categories: Error Statistics | Tags: | 3 Comments

Philosophy of Science Assoc. (PSA) symposium on Philosophy of Statistics in the Higgs Experiments “How Many Sigmas to Discovery?”

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The biennial meeting of the Philosophy of Science Association (PSA) starts this week (Nov. 6-9) in Chicago, together with the History of Science Society. I’ll be part of the symposium:

 

How Many Sigmas to Discovery?
Philosophy and Statistics in the Higgs Experiments

 

on Nov.8 with Robert Cousins, Allan Franklin, and Kent Staley. If you’re in the neighborhood stop by.

 

Summary

“A 5 sigma effect!” is how the recent Higgs boson discovery was reported. Yet before the dust had settled, the very nature and rationale of the 5 sigma (or 5 standard deviation) discovery criteria began to be challenged and debated both among scientists and in the popular press. Why 5 sigma? How is it to be interpreted? Do p-values in high-energy physics (HEP) avoid controversial uses and misuses of p-values in social and other sciences? The goal of our symposium is to combine the insights of philosophers and scientists whose work interrelates philosophy of statistics, data analysis and modeling in experimental physics, with critical perspectives on how discoveries proceed in practice. Our contributions will link questions about the nature of statistical evidence, inference, and discovery with questions about the very creation of standards for interpreting and communicating statistical experiments. We will bring out some unique aspects of discovery in modern HEP. We also show the illumination the episode offers to some of the thorniest issues revolving around statistical inference, frequentist and Bayesian methods, and the philosophical, technical, social, and historical dimensions of scientific discovery.

   Questions:

1) How do philosophical problems of statistical inference interrelate with debates about inference and modeling in high energy physics (HEP)?

2) Have standards for scientific discovery in particle physics shifted? And if so, how has this influenced when a new phenomenon is “found”?

3) Can understanding the roles of statistical hypotheses tests in HEP resolve classic problems about their justification in both physical and social sciences?

4) How do pragmatic, epistemic and non-epistemic values and risks influence the collection, modeling, and interpretation of data in HEP?

 

Abstracts for Individual Presentations

robert cousins(1) Unresolved Philosophical Issues Regarding Hypothesis Testing in High Energy Physics
Robert D. Cousins.
Professor, Department of Physics and Astronomy, University of California, Los Angeles (UCLA)

The discovery and characterization of a Higgs boson in 2012-2013 provide multiple examples of statistical inference as practiced in high energy physics (elementary particle physics).  The main methods employed have a decidedly frequentist flavor, drawing in a pragmatic way on both Fisher’s ideas and the Neyman-Pearson approach.  A physics model being tested typically has a “law of nature” at its core, with parameters of interest representing masses, interaction strengths, and other presumed “constants of nature”.  Additional “nuisance parameters” are needed to characterize the complicated measurement processes.  The construction of confidence intervals for a parameter of interest q is dual to hypothesis testing, in that the test of the null hypothesis q=q0 at significance level (“size”) a is equivalent to whether q0 is contained in a confidence interval for q with confidence level (CL) equal to 1-a.  With CL or a specified in advance (“pre-data”), frequentist coverage properties can be assured, at least approximately, although nuisance parameters bring in significant complications.  With data in hand, the post-data p-value can be defined as the smallest significance level a at which the null hypothesis would be rejected, had that a been specified in advance.  Carefully calculated p-values (not assuming normality) are mapped onto the equivalent number of standard deviations (“s”) in a one-tailed test of the mean of a normal distribution. For a discovery such as the Higgs boson, experimenters report both p-values and confidence intervals of interest. Continue reading

Categories: Error Statistics, Higgs, P-values | Tags: | 18 Comments

Gelman recognizes his error-statistical (Bayesian) foundations

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From Gelman’s blog:

“In one of life’s horrible ironies, I wrote a paper “Why we (usually) don’t have to worry about multiple comparisons” but now I spend lots of time worrying about multiple comparisons”

Posted by  on

Exhibit A: [2012] Why we (usually) don’t have to worry about multiple comparisons. Journal of Research on Educational Effectiveness 5, 189-211. (Andrew Gelman, Jennifer Hill, and Masanao Yajima)

Exhibit B: The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time, in press. (Andrew Gelman and Eric Loken) (Shortened version is here.)

 

The “forking paths” paper, in my reading,  basically argues that mere hypothetical possibilities about what you would or might have done had the data been different (in order to secure a desired interpretation) suffices to alter the characteristics of the analysis you actually did. That’s an error statistical argument–maybe even stronger than what some error statisticians would say. What’s really being condemned are overly flexible ways to move from statistical results to substantive claims. The p-values are illicit when taken to provide evidence for those claims because an actual p-value requires Prob(P < p;Ho) = p (and the actual p-value has become much greater by design). The criticism makes perfect sense if you’re scrutinizing inferences according to how well or severely tested they are. Actual error probabilities are accordingly altered or unable to be calculated. However, if one is going to scrutinize inferences according to severity then the same problematic flexibility would apply to Bayesian analyses, whether or not they have a way to pick up on it. (It’s problematic if they don’t.) I don’t see the magic by which a concern for multiple testing disappears in Bayesian analysis (e.g., in the first paper) except by assuming some prior takes care of it.

See my comment here.

Categories: Error Statistics, Gelman | 17 Comments

“The Supernal Powers Withhold Their Hands And Let Me Alone” : C.S. Peirce

C. S. Peirce: 10 Sept, 1839-19 April, 1914

C. S. Peirce: 10 Sept, 1839-19 April, 1914

Memory Lane* in Honor of C.S. Peirce’s Birthday:
(Part 3) of “Peircean Induction and the Error-Correcting Thesis”

Deborah G. Mayo
Transactions of the Charles S. Peirce Society 41(2) 2005: 299-319

(9/10) Peircean Induction and the Error-Correcting Thesis (Part I)

(9/10) (Part 2) Peircean Induction and the Error-Correcting Thesis

8. Random sampling and the uniformity of nature

We are now at the point to address the final move in warranting Peirce’s [self-correcting thesis] SCT. The severity or trustworthiness assessment, on which the error correcting capacity depends, requires an appropriate link (qualitative or quantitative) between the data and the data generating phenomenon, e.g., a reliable calibration of a scale in a qualitative case, or a probabilistic connection between the data and the population in a quantitative case. Establishing such a link, however, is regarded as assuming observed regularities will persist, or making some “uniformity of nature” assumption—the bugbear of attempts to justify induction.

But Peirce contrasts his position with those favored by followers of Mill, and “almost all logicians” of his day, who “commonly teach that the inductive conclusion approximates to the truth because of the uniformity of nature” (2.775). Inductive inference, as Peirce conceives it (i.e., severe testing) does not use the uniformity of nature as a premise. Rather, the justification is sought in the manner of obtaining data. Justifying induction is a matter of showing that there exist methods with good error probabilities. For this it suffices that randomness be met only approximately, that inductive methods check their own assumptions, and that they can often detect and correct departures from randomness.

… It has been objected that the sampling cannot be random in this sense. But this is an idea which flies far away from the plain facts. Thirty throws of a die constitute an approximately random sample of all the throws of that die; and that the randomness should be approximate is all that is required. (1.94)

Peirce backs up his defense with robustness arguments. For example, in an (attempted) Binomial induction, Peirce asks, “what will be the effect upon inductive inference of an imperfection in the strictly random character of the sampling” (2.728). What if, for example, a certain proportion of the population had twice the probability of being selected? He shows that “an imperfection of that kind in the random character of the sampling will only weaken the inductive conclusion, and render the concluded ratio less determinate, but will not necessarily destroy the force of the argument completely” (2.728). This is particularly so if the sample mean is near 0 or 1. In other words, violating experimental assumptions may be shown to weaken the trustworthiness or severity of the proceeding, but this may only mean we learn a little less.

Yet a further safeguard is at hand:

Nor must we lose sight of the constant tendency of the inductive process to correct itself. This is of its essence. This is the marvel of it. …even though doubts may be entertained whether one selection of instances is a random one, yet a different selection, made by a different method, will be likely to vary from the normal in a different way, and if the ratios derived from such different selections are nearly equal, they may be presumed to be near the truth. (2.729)

Here, the marvel is an inductive method’s ability to correct the attempt at random sampling. Still, Peirce cautions, we should not depend so much on the self-correcting virtue that we relax our efforts to get a random and independent sample. But if our effort is not successful, and neither is our method robust, we will probably discover it. “This consideration makes it extremely advantageous in all ampliative reasoning to fortify one method of investigation by another” (ibid.). Continue reading

Categories: C.S. Peirce, Error Statistics, phil/history of stat | 11 Comments

Higgs discovery two years on (2: Higgs analysis and statistical flukes)

Higgs_cake-sI’m reblogging a few of the Higgs posts, with some updated remarks, on this two-year anniversary of the discovery. (The first was in my last post.) The following, was originally “Higgs Analysis and Statistical Flukes: part 2″ (from March, 2013).[1]

Some people say to me: “This kind of reasoning is fine for a ‘sexy science’ like high energy physics (HEP)”–as if their statistical inferences are radically different. But I maintain that this is the mode by which data are used in “uncertain” reasoning across the entire landscape of science and day-to-day learning (at least, when we’re trying to find things out)[2] Even with high level theories, the particular problems of learning from data are tackled piecemeal, in local inferences that afford error control. Granted, this statistical philosophy differs importantly from those that view the task as assigning comparative (or absolute) degrees-of-support/belief/plausibility to propositions, models, or theories.  Continue reading

Categories: Higgs, highly probable vs highly probed, P-values, Severity, Statistics | 13 Comments

“Statistical Science and Philosophy of Science: where should they meet?”

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Four score years ago (!) we held the conference “Statistical Science and Philosophy of Science: Where Do (Should) They meet?” at the London School of Economics, Center for the Philosophy of Natural and Social Science, CPNSS, where I’m visiting professor [1] Many of the discussions on this blog grew out of contributions from the conference, and conversations initiated soon after. The conference site is here; my paper on the general question is here.[2]

My main contribution was “Statistical Science Meets Philosophy of Science Part 2: Shallow versus Deep Explorations” SS & POS 2. It begins like this: 

1. Comedy Hour at the Bayesian Retreat[3]

 Overheard at the comedy hour at the Bayesian retreat: Did you hear the one about the frequentist… Continue reading

Categories: Error Statistics, Philosophy of Statistics, Severity, Statistics, StatSci meets PhilSci | 23 Comments

A. Spanos: “Recurring controversies about P values and confidence intervals revisited”

A SPANOS

Aris Spanos
Wilson E. Schmidt Professor of Economics
Department of Economics, Virginia Tech

Recurring controversies about P values and confidence intervals revisited*
Ecological Society of America (ESA) ECOLOGY
Forum—P Values and Model Selection (pp. 609-654)
Volume 95, Issue 3 (March 2014): pp. 645-651

INTRODUCTION

The use, abuse, interpretations and reinterpretations of the notion of a P value has been a hot topic of controversy since the 1950s in statistics and several applied fields, including psychology, sociology, ecology, medicine, and economics.

The initial controversy between Fisher’s significance testing and the Neyman and Pearson (N-P; 1933) hypothesis testing concerned the extent to which the pre-data Type  I  error  probability  α can  address the arbitrariness and potential abuse of Fisher’s post-data  threshold for the value. Continue reading

Categories: CIs and tests, Error Statistics, Fisher, P-values, power, Statistics | 32 Comments

Who ya gonna call for statistical Fraudbusting? R.A. Fisher, P-values, and error statistics (again)

images-9If there’s somethin’ strange in your neighborhood. Who ya gonna call?(Fisherian Fraudbusters!)*

*[adapted from R. Parker’s “Ghostbusters”]

When you need to warrant serious accusations of bad statistics, if not fraud, where do scientists turn? Answer: To the frequentist error statistical reasoning and to p-value scrutiny, first articulated by R.A. Fisher[i].The latest accusations of big time fraud in social psychology concern the case of Jens Förster. As Richard Gill notes:

The methodology here is not new. It goes back to Fisher (founder of modern statistics) in the 30’s. Many statistics textbooks give as an illustration Fisher’s re-analysis (one could even say: meta-analysis) of Mendel’s data on peas. The tests of goodness of fit were, again and again, too good. There are two ingredients here: (1) the use of the left-tail probability as p-value instead of the right-tail probability. (2) combination of results from a number of independent experiments using a trick invented by Fisher for the purpose, and well known to all statisticians. (Richard D. Gill)

Continue reading

Categories: Error Statistics, Fisher, significance tests, Statistical fraudbusting, Statistics | 42 Comments

A. Spanos: Talking back to the critics using error statistics (Phil6334)

spanos 2014

Aris Spanos’ overview of error statistical responses to familiar criticisms of statistical tests. Related reading is Mayo and Spanos (2011)

Categories: Error Statistics, frequentist/Bayesian, Phil6334, reforming the reformers, statistical tests, Statistics | Leave a comment

Phil 6334: Foundations of statistics and its consequences: Day #12

picture-216-1We interspersed key issues from the reading for this session (from Howson and Urbach) with portions of my presentation at the Boston Colloquium (Feb, 2014): Revisiting the Foundations of Statistics in the Era of Big Data: Scaling Up to Meet the Challenge. (Slides below)*.

Someone sent us a recording  (mp3)of the panel discussion from that Colloquium (there’s a lot on “big data” and its politics) including: Mayo, Xiao-Li Meng (Harvard), Kent Staley (St. Louis), and Mark van der Laan (Berkeley). 

See if this works: | mp3

*There’s a prelude here to our visitor on April 24: Professor Stanley Young from the National Institute of Statistical Sciences.

 

Categories: Bayesian/frequentist, Error Statistics, Phil6334 | 43 Comments

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