Monthly Archives: June 2013

Blog Contents: mid-year

Error Statistics Philosophy BLOG: Table of Contents 2013 (January-June)*

img_02443January 2013

(1/2) Severity as a ‘Metastatistical’ Assessment
(1/4) Severity Calculator
(1/6) Guest post: Bad Pharma? (S. Senn)
(1/9) RCTs, skeptics, and evidence-based policy
(1/10) James M. Buchanan
(1/11) Aris Spanos: James M. Buchanan: a scholar, teacher and friend
(1/12) Error Statistics Blog: Table of Contents
(1/15) Ontology & Methodology: Second call for Abstracts, Papers
(1/18) New Kvetch/PhilStock
(1/19) Saturday Night Brainstorming and Task Forces: (2013) TFSI on NHST
(1/22) New PhilStock
(1/23) P-values as posterior odds?
(1/26) Coming up: December U-Phil Contributions….
(1/27) U-Phil: S. Fletcher & N. Jinn
(1/30) U-Phil: J. A. Miller: Blogging the SLP Continue reading

Categories: Metablog, Statistics | Leave a comment

Palindrome “contest” contest

 metablog old fashion typewriterWant to win one of these books? You may not have noticed that since May, the palindrome rules have gotten trivially easy. So since it’s Saturday night, and I’m giving a time extension to 14 July – Le Quatorze juillet—have some fun coming up with a palindrome. It only needs to include “Elba” and the word “contest”. For full bibiographies and complete rules, see palindrome page:

 .EGEK CoverSend your candidates to me at One of the winners under the older, much harder, rules is here.

Previous palindrome contests included:

runs test, omnibus, cycle, dominate, editor, data, Model, sample, random, probable, Bayes, confident, likely, error, decision, variable, integrate, maximal, median (comedian), interpret, action, code, predict, luck, assess, model, simple, null, bootstrap,minimum, wrong, prefer, dogma, (s)exist, email

with variations.

Categories: Announcement, Palindrome | Leave a comment

Why I am not a “dualist” in the sense of Sander Greenland


This post picks up, and continues, an exchange that began with comments on my June 14 blogpost (between Sander Greenland, Nicole Jinn, and I). My new response is at the end. The concern is how to expose and ideally avoid some of the well known flaws and foibles in statistical inference, thanks to gaps between data and statistical inference, and between statistical inference and substantive claims. I am not rejecting the use of multiple methods in the least (they are highly valuable when one method is capable of detecting or reducing flaws in one or more others). Nor am I speaking of classical dualism in metaphysics (which I also do not espouse). I begin with Greenland’s introduction of this idea in his comment… (For various earlier comments, see the post.)

Sander Greenland 

. I sense some confusion of criticism of the value of tests as popular tools vs. criticism of their logical foundation. I am a critic in the first, practical category, who regards the adoption of testing outside of narrow experimental programs as an unmitigated disaster, resulting in publication bias, prosecutor-type fallacies, and affirming the consequent fallacies throughout the health and social science literature. Even though testing can in theory be used soundly, it just hasn’t done well in practice in these fields. This could be ascribed to human failings rather than failings of received testing theories, but I would require any theory of applied statistics to deal with human limitations, just as safety engineering must do for physical products. I regard statistics as having been woefully negligent of cognitive psychology in this regard. In particular, widespread adoption and vigorous defense of a statistical method or philosophy is no more evidence of its scientific value than widespread adoption and vigorous defense of a religion is evidence of its scientific value. 
That should bring us to alternatives. I am aware of no compelling data showing that other approaches would have done better, but I do find compelling the arguments that at least some of the problems would have been mitigated by teaching a dualist approach to statistics, in which every procedure must be supplied with both an accurate frequentist and an accurate Bayesian interpretation, if only to reduce prevalent idiocies like interpreting a two-sided P-value as “the” posterior probability of a point null hypothesis.

 Nicole Jinn
 (to Sander Greenland)

 What exactly is this ‘dualist’ approach to teaching statistics and why does it mitigate the problems, as you claim? (I am increasingly interested in finding more effective ways to teach/instruct others in various age groups about statistics.)
I have a difficult time seeing how effective this ‘dualist’ way of teaching could be for the following reason: the Bayesian and frequentist approaches are vastly different in their aims and the way they see statistics being used in (natural or social) science, especially when one looks more carefully at the foundations of each methodology (e.g., disagreements about where exactly probability enters into inference, or about what counts as relevant information). Hence, it does not make sense (to me) to supply both types of interpretation to the same data and the same research question! Instead, it makes more sense (from a teaching perspective) to demonstrate a Bayesian interpretation for one experiment, and a frequentist interpretation for another experiment, in the hopes of getting at the (major) differences between the two methodologies.


Sander. Thanks for your comment. 
Interestingly, I think the conglomeration of error statistical tools are the ones most apt at dealing with human limitations and foibles: they give piecemeal methods to ask one question at a time (e.g., would we be mistaken to suppose there is evidence of any effect at all? mistaken about how large? about iid assumptions? about possible causes? about implications for distinguishing any theories?). The standard Bayesian apparatus requires setting out a complete set of hypotheses that might arise, plus prior probabilities in each of them (or in “catchall” hypotheses), as well as priors in the model…and after this herculean task is complete, there is a purely deductive update: being deductive it never goes beyond the givens. Perhaps the data will require a change in your prior—this is what you must have believed before, since otherwise you find your posterior unacceptable—thereby encouraging the very self-sealing inferences we all claim to deplore. Continue reading

Categories: Bayesian/frequentist, Error Statistics, P-values, Statistics | 21 Comments

What do these share in common: m&ms, limbo stick, ovulation, Dale Carnegie? Sat night potpourri


For entertainment only

I had said I would label as pseudoscience or questionable science any enterprise that regularly permits the kind of ‘verification biases’ in the laundry list of my June 1 post.  How regularly? (I’ve been asked)

Well, surely if it’s as regular as, say, much of social psychology, it goes over the line. But it’s not mere regularity, it’s the nature of the data, the type of inferences being drawn, and the extent of self-scrutiny and recognition of errors shown (or not shown). The regularity is just a consequence of the methodological holes. My standards may be considerably more stringent than most, but quite aside from statistical issues, I simply do not find hypotheses well-tested if they are based on “experiments” that consist of giving questionnaires. At least not without a lot more self-scrutiny and discussion of flaws than I ever see. (There may be counterexamples.)

Attempts to recreate phenomena of interest in typical social science “labs” leave me with the same doubts. Huge gaps often exist between elicited and inferred results. One might locate the problem under “external validity” but to me it is just the general problem of relating statistical data to substantive claims.

Experimental economists (expereconomists) take lab results plus statistics to warrant sometimes ingenious inferences about substantive hypotheses.  Vernon Smith (of the Nobel Prize in Econ) is rare in subjecting his own results to “stress tests”.  I’m not withdrawing the optimistic assertions he cites from EGEK (Mayo 1996) on Duhem-Quine (e.g., from “Rhetoric and Reality” 2001, p. 29). I’d still maintain, “Literal control is not needed to attribute experimental results correctly (whether to affirm or deny a hypothesis). Enough experimental knowledge will do”.  But that requires piece-meal strategies that accumulate, and at least a little bit of “theory” and/or a decent amount of causal understanding.[1]

I think the generalizations extracted from questionnaires allow for an enormous amount of “reading into” the data. Suddenly one finds the “best” explanation. Questionnaires should be deconstructed for how they may be misinterpreted, not to mention how responders tend to guess what the experimenter is looking for. (I’m reminded of the current hoopla over questionnaires on breadwinners, housework and divorce rates!) I respond with the same eye-rolling to just-so story telling along the lines of evolutionary psychology.

I apply the “Stapel test”: Even if Stapel had bothered to actually carry out the data-collection plans that he so carefully crafted, I would not find the inferences especially telling in the least. Take for example the planned-but-not-implemented study discussed in the recent New York Times article on Stapel:

 Stapel designed one such study to test whether individuals are inclined to consume more when primed with the idea of capitalism. He and his research partner developed a questionnaire that subjects would have to fill out under two subtly different conditions. In one, an M&M-filled mug with the word “kapitalisme” printed on it would sit on the table in front of the subject; in the other, the mug’s word would be different, a jumble of the letters in “kapitalisme.” Although the questionnaire included questions relating to capitalism and consumption, like whether big cars are preferable to small ones, the study’s key measure was the amount of M&Ms eaten by the subject while answering these questions….Stapel and his colleague hypothesized that subjects facing a mug printed with “kapitalisme” would end up eating more M&Ms.

Stapel had a student arrange to get the mugs and M&Ms and later load them into his car along with a box of questionnaires. He then drove off, saying he was going to run the study at a high school in Rotterdam where a friend worked as a teacher.

Stapel dumped most of the questionnaires into a trash bin outside campus. At home, using his own scale, he weighed a mug filled with M&Ms and sat down to simulate the experiment. While filling out the questionnaire, he ate the M&Ms at what he believed was a reasonable rate and then weighed the mug again to estimate the amount a subject could be expected to eat. He built the rest of the data set around that number. He told me he gave away some of the M&M stash and ate a lot of it himself. “I was the only subject in these studies,” he said.

He didn’t even know what a plausible number of M&Ms consumed would be! But never mind that, observing a genuine “effect” in this silly study would not have probed the hypothesis. Would it? Continue reading

Categories: junk science, Statistics | 5 Comments

Stanley Young: better p-values through randomization in microarrays

I wanted to locate some uncluttered lounge space for one of the threads to emerge in comments from 6/14/13. Thanks to Stanley Young for permission to post this. 

YoungPhoto2008 S. Stanley Young, PhD
Assistant Director for Bioinformatics
National Institute of Statistical Sciences
Research Triangle Park, NC

There is a relatively unknown problem with microarray experiments, in addition to the multiple testing problems. Samples should be randomized over important sources of variation; otherwise p-values may be flawed. Until relatively recently, the microarray samples were not sent through assay equipment in random order. Clinical trial statisticians at GSK insisted that the samples go through assay in random order. Rather amazingly the data became less messy and p-values became more orderly. The story is given here:
Essentially all the microarray data pre-2010 is unreliable. For another example, Mass spec data was analyzed Petrocoin. The samples were not randomized that claims with very small p-values failed to replicate. See K.A. Baggerly et al., “Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments,” Bioinformatics, 20:777-85, 2004. So often the problem is not with p-value technology, but with the design and conduct of the study.


Please check other comments on microarrays from 6/14/13.

Categories: P-values, Statistics | Tags: , , | 9 Comments

PhilStock: The Great Taper Caper

stock picture smaillSee Rejected Posts.

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P-values can’t be trusted except when used to argue that P-values can’t be trusted!

images-1Have you noticed that some of the harshest criticisms of frequentist error-statistical methods these days rest on methods and grounds that the critics themselves purport to reject? Is there a whiff of inconsistency in proclaiming an “anti-hypothesis-testing stance” while in the same breath extolling the uses of statistical significance tests and p-values in mounting criticisms of significance tests and p-values? I was reminded of this in the last two posts (comments) on this blog (here and here) and one from Gelman from a few weeks ago (“Interrogating p-values”).

Gelman quotes from a note he is publishing:

“..there has been a growing sense that psychology, biomedicine, and other fields are being overwhelmed with errors … . In two recent series of papers, Gregory Francis and Uri Simonsohn and collaborators have demonstrated too-good-to-be-true patterns of p-values in published papers, indicating that these results should not be taken at face value.”

But this fraudbusting is based on finding statistically significant differences from null hypotheses (e.g., nulls asserting random assignments of treatments)! If we are to hold small p-values untrustworthy, we would be hard pressed to take them as legitimating these criticisms, especially those of a career-ending sort.

…in addition to the well-known difficulties of interpretation of p-values…,…and to the problem that, even when all comparisons have been openly reported and thus p-values are mathematically correct, the ‘statistical significance filter’ ensures that estimated effects will be in general larger than true effects, with this discrepancy being well over an order of magnitude in settings where the true effects are small… (Gelman 2013)

But surely anyone who believed this would be up in arms about using small p-values as evidence of statistical impropriety. Am I the only one wondering about this?*

CLARIFICATION (6/15/13): Corey’s comment today leads me to a clarification, lest anyone misunderstand my point. I am sure that Francis, Simonsohn and others would never be using p-values and associated methods in the service of criticism if they did not regard the tests as legitimate scientific tools. I wasn’t talking about them. I was alluding to critics of tests who point to their work as evidence the statistical tools are not legitimate. Now maybe Gelman only intends to say, what we know and agree with, that tests can be misused and misinterpreted. But in these comments, our exchanges, and elsewhere, it is clear he is saying something much stronger. In my view, the use of significance tests by debunkers should have been taken as strong support for the value of the tools, correctly used. In short, I thought it was a success story! and I was rather perplexed to see somewhat the reverse.


*This just in: If one wants to see a genuine quack extremist** who was outed long ago***, see Ziliac’s article declaring the Higgs physicists are pseudoscientists for relying on significance levels!( in the Financial Post 6/12/13).

**I am not placing the critics referred to above under this umbrella in the least.

***For some reviews of Ziliac and McCloskey, see widgets on left. For their flawed testimony on the Matrixx case, please search this blog.

Categories: reforming the reformers, Statistical fraudbusting, Statistics | 43 Comments

Mayo: comment on the repressed memory research

freud mirror espHere are some reflections on the repressed memory articles from Richard Gill’s post, focusing on Geraerts,,(2008).

1. Richard Gill reported that “Everyone does it this way, in fact, if you don’t, you’d never get anything published: …People are not deliberately cheating: they honestly believe in their theories and believe the data is supporting them and are just doing their best to make this as clear as possible to everyone.”

This remark is very telling. I recommend we just regard those cases as illustrating a theory one believes, rather than providing evidence for that theory. If we could mark them as such, we can stop blaming significance tests for playing a role in what are actually only illustrative attempts, or to strengthen someone’s beliefs about a theory.

2. I was surprised the examples had to do with recovered memories. Wasn’t that entire area dubbed a pseudoscience way back (at least 15-25 years ago?) when “therapy induced” memories of childhood sexual abuse (CSA) were discovered to be just that—therapy induced and manufactured? After the witch hunts that ensued (the very accusation sufficing for evidence), I thought the field of “research” had been put out of its and our misery. So, aside from having used the example in a course on critical thinking, I’m not up on this current work at all. But, as these are just blog comments, let me venture some off-the-cuff skeptical thoughts. They will have almost nothing to do with the statistical data analysis, by the way…

3. Geraerts,, (2008, 22) admit at the start of the article that therapy-recovered CSA memories are unreliable, and the idea of automatically repressing a traumatic event like CSA implausible. Then mightn’t it seem the entire research program should be dropped? Not to its adherents! As with all theories that enjoy the capacity of being sufficiently flexible to survive anomaly (Popper’s pseudosciences), there’s some life left here too. Maybe , its adherents reason, it’s not necessary for those who report “spontaneously recovered” CSA memories to be repressors, instead they merely be “suppressors” who are good at blocking out negative events. If so, they didn’t automatically repress but rather deliberately suppressed: “Our findings may partly explain why people with spontaneous CSA memories have the subjective impression that they have ‘repressed’ their CSA memories for many years.” (ibid., 22).

4. Shouldn’t we stop there? I would. We have a research program growing out of an exemplar of pseudoscience being kept alive by ever-new “monster-barring” strategies (as Lakatos called them). (I realize they’re not planning to go out to the McMartin school, but still…) If a theory T is flexible enough so that any observations can be interpreted through it, and thereby regarded as confirming T, then it is no surprise that this is still true when the instances are dressed up with statistics. It isn’t that theories of repressed memories are implausible or improbable (in whatever sense one takes those terms). It is the ever-flexibility of these theories that renders the research program pseudoscience (along with, in this case, a history of self-sealing data interpretations). Continue reading

Categories: junk science, Statistical fraudbusting, Statistics | 7 Comments

Richard Gill: “Integrity or fraud… or just quesionable research practices?”

Professor Gill

Professor Gill

Professor Richard Gill
Statistics Group
Mathematical Institute
Leiden University

I am very grateful to Richard Gill for permission to post an e-mail from him (after my “dirty laundry” post) along with slides from his talk, “Integrity or fraud… or just questionable research practices?” and associated papers. I record my own reflections on the pseudoscientific nature of the program in one of the Geraerts, papers in a later post.

I certainly have been thinking about these issues a lot in recent months. I got entangled in intensive scientific and media discussions – mainly confined to the Netherlands  – concerning the cases of social psychologist Dirk Smeesters and of psychologist Elke Geraerts.  See:

And I recently got asked to look at the statistics in some papers of another … [researcher] ..but this one is still confidential ….

The verdict on Smeesters was that he like Stapel actually faked data (though he still denies this).

The Geraerts case is very much open, very much unclear. The senior co-authors Merckelbach, McNally of the attached paper, published in the journal “Memory”, have asked the journal editors for it to be withdrawn because they suspect the lead author, Elke Geraerts, of improper conduct. She denies any impropriety. It turns out that none of the co-authors have the data. Legally speaking it belongs to the University of Maastricht where the research was carried out and where Geraerts was a promising postdoc in Merckelbach’s group. She later got a chair at Erasmus University Rotterdam and presumably has the data herself but refuses to share it with her old co-authors or any other interested scientists. Just looking at the summary statistics in the paper one sees evidence of “too good to be true”. Average scores in groups supposed in theory to be similar are much closer to one another than one would expect on the basis of the within group variation (the paper reports averages and standard deviations for each group, so it is easy to compute the F statistic for equality of the three similar groups and use its left tail probability as test statistic. Continue reading

Categories: junk science, Statistical fraudbusting, Statistics | 5 Comments

Anything Tests Can do, CIs do Better; CIs Do Anything Better than Tests?* (reforming the reformers cont.)

Having reblogged the 5/17/12 post on “reforming the reformers” yesterday, I thought I should reblog its follow-up: 6/2/12.

Consider again our one-sided Normal test T+, with null H0: μ < μ0 vs μ >μ0  and  μ0 = 0,  α=.025, and σ = 1, but let n = 25. So M is statistically significant only if it exceeds .392. Suppose M (the sample mean) just misses significance, say

Mo = .39.

The flip side of a fallacy of rejection (discussed before) is a fallacy of acceptance, or the fallacy of misinterpreting statistically insignificant results.  To avoid the age-old fallacy of taking a statistically insignificant result as evidence of zero (0) discrepancy from the null hypothesis μ =μ0, we wish to identify discrepancies that can and cannot be ruled out.  For our test T+, we reason from insignificant results to inferential claims of the form:

μ < μ0 + γ

Fisher continually emphasized that failure to reject was not evidence for the null.  Neyman, we saw, in chastising Carnap, argued for the following kind of power analysis:

Neymanian Power Analysis (Detectable Discrepancy Size DDS): If data x are not statistically significantly different from H0, and the power to detect discrepancy γ is high (low), then x constitutes good (poor) evidence that the actual effect is < γ. (See 11/9/11 post).

By taking into account the actual x0, a more nuanced post-data reasoning may be obtained.

“In the Neyman-Pearson theory, sensitivity is assessed by means of the power—the probability of reaching a preset level of significance under the assumption that various alternative hypotheses are true. In the approach described here, sensitivity is assessed by means of the distribution of the random variable P, considered under the assumption of various alternatives. “ (Cox and Mayo 2010, p. 291):

This may be captured in :

FEV(ii): A moderate p-value is evidence of the absence of a discrepancy d from Ho only if there is a high probability the test would have given a worse fit with H0 (i.e., a smaller p value) were a discrepancy d to exist. (Mayo and Cox 2005, 2010, 256).

This is equivalently captured in the Rule of Acceptance (Mayo (EGEK) 1996, and in the severity interpretation for acceptance, SIA, Mayo and Spanos (2006, p. 337):

SIA: (a): If there is a very high probability that [the observed difference] would have been larger than it is, were μ > μ1, then μ < μ1 passes the test with high severity,…

But even taking tests and CIs just as we find them, we see that CIs do not avoid the fallacy of acceptance: they do not block erroneous construals of negative results adequately. Continue reading

Categories: CIs and tests, Error Statistics, reformers, Statistics | Tags: , , , , , , , | Leave a comment

PhilStock: Topsy-Turvy Game

stock picture smaillSee rejected posts.  

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Do CIs Avoid Fallacies of Tests? Reforming the Reformers (Reblog 5/17/12)

The one method that enjoys the approbation of the New Reformers is that of confidence intervals. The general recommended interpretation is essentially this:

For a reasonably high choice of confidence level, say .95 or .99, values of µ within the observed interval are plausible, those outside implausible.

Geoff Cumming, a leading statistical reformer in psychology, has long been pressing for ousting significance tests (or NHST[1]) in favor of CIs. The level of confidence “specifies how confident we can be that our CI includes the population parameter m (Cumming 2012, p.69). He recommends prespecified confidence levels .9, .95 or .99:

“We can say we’re 95% confident our one-sided interval includes the true value. We can say the lower limit (LL) of the one-sided CI…is a likely lower bound for the true value, meaning that for 5% of replications the LL will exceed the true value. “ (Cumming 2012, p. 112)[2]

For simplicity, I will use the 2-standard deviation cut-off corresponding to the one-sided confidence level of ~.98.

However, there is a duality between tests and intervals (the intervals containing the parameter values not rejected at the corresponding level with the given data).[3]

“One-sided CIs are analogous to one-tailed tests but, as usual, the estimation approach is better.”

Is it?   Consider a one-sided test of the mean of a Normal distribution with n iid samples, and known standard deviation σ, call it test T+.

H0: µ ≤  0 against H1: µ >  0 , and let σ= 1.

Test T+ at significance level .02 is analogous to forming the one-sided (lower) 98% confidence interval:

µ > M – 2(1/ √n ).

where M, following Cumming, is the sample mean (thereby avoiding those x-bars). M – 2(1/ √n ) is the lower limit (LL) of a 98% CI.

Central problems with significance tests (whether of the N-P or Fisherian variety) include:

(1) results are too dichotomous (e.g., significant at a pre-set level or not);

(2) two equally statistically significant results but from tests with different sample sizes are reported in the same way  (whereas the larger the sample size the smaller the discrepancy the test is able to detect);

(3) significance levels (even observed p-values) fail to indicate the extent of the effect or discrepancy (in the case of test T+ , in the positive direction).

We would like to know for what values of δ it is warranted to infer  µ > µ0 + δ. Continue reading

Categories: confidence intervals and tests, reformers, Statistics | Tags: , , , | 7 Comments

Some statistical dirty laundry

Objectivity 1: Will the Real Junk Science Please Stand Up?I finally had a chance to fully read the 2012 Tilberg Report* on “Flawed Science” last night. The full report is now here. Here are some stray thoughts…

1. Slipping into pseudoscience.
The authors of the Report say they never anticipated giving a laundry list of “undesirable conduct” by which researchers can flout pretty obvious requirements for the responsible practice of science. It was an accidental byproduct of the investigation of one case (Diederik Stapel, social psychology) that they walked into a culture of “verification bias”[1]. Maybe that’s why I find it so telling. It’s as if they could scarcely believe their ears when people they interviewed “defended the serious and less serious violations of proper scientific method with the words: that is what I have learned in practice; everyone in my research environment does the same, and so does everyone we talk to at international conferences” (Report 48). So they trot out some obvious rules, and it seems to me that they do a rather good job.

One of the most fundamental rules of scientific research is that an investigation must be designed in such a way that facts that might refute the research hypotheses are given at least an equal chance of emerging as do facts that confirm the research hypotheses. Violations of this fundamental rule, such as continuing an experiment until it works as desired, or excluding unwelcome experimental subjects or results, inevitably tends to confirm the researcher’s research hypotheses, and essentially render the hypotheses immune to the facts…. [T]he use of research procedures in such a way as to ‘repress’ negative results by some means” may be called verification bias. [my emphasis] (Report, 48).

I would place techniques for ‘verification bias’ under the general umbrella of techniques for squelching stringent criticism and repressing severe tests. These gambits make it so easy to find apparent support for one’s pet theory or hypotheses, as to count as no evidence at all (see some from their list ). Any field that regularly proceeds this way I would call a pseudoscience, or non-science, following Popper. “Observations or experiments can be accepted as supporting a theory (or a hypothesis, or a scientific assertion) only if these observations or experiments are severe tests of the theory” (Popper 1994, p. 89). [2] It is unclear at what point a field slips into the pseudoscience realm.

2. A role for philosophy of science?
I am intrigued that one of the final recommendations in the Report is this:

In the training program for PhD students, the relevant basic principles of philosophy of science, methodology, ethics and statistics that enable the responsible practice of science must be covered. Based on these insights, research Master’s students and PhD students must receive practical training from their supervisors in the application of the rules governing proper and honest scientific research, which should include examples of such undesirable conduct as data massage. The Graduate School must explicitly ensure that this is implemented.

A philosophy department could well create an entire core specialization that revolved around “the relevant basic principles of philosophy of science, methodology, ethics and statistics that enable the responsible practice of science” (ideally linked with one or more other departments).  That would be both innovative and fill an important gap, it seems to me. Is anyone doing this?

3. Hanging out some statistical dirty laundry.images
Items in their laundry list include:

  • An experiment fails to yield the expected statistically significant results. The experiment is repeated, often with minor changes in the manipulation or other conditions, and the only experiment subsequently reported is the one that did yield the expected results. The article makes no mention of this exploratory method… It should be clear, certainly with the usually modest numbers of experimental subjects, that using experiments in this way can easily lead to an accumulation of chance findings…. Continue reading
Categories: junk science, spurious p values, Statistics | 6 Comments

Winner of May Palindrome Contest

“Able no one nil red nudist opening nine pots. I’d underline ‘No’ on Elba.”  Anonymous. See rejected posts.

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