Monthly Archives: June 2015

Stapel’s Fix for Science? Admit the story you want to tell and how you “fixed” the statistics to support it!



Stapel’s “fix” for science is to admit it’s all “fixed!”

That recent case of the guy suspected of using faked data for a study on how to promote support for gay marriage in a (retracted) paper, Michael LaCour, is directing a bit of limelight on our star fraudster Diederik Stapel (50+ retractions).

The Chronicle of Higher Education just published an article by Tom Bartlett:Can a Longtime Fraud Help Fix Science? You can read his full interview of Stapel here. A snippet:

You write that “every psychologist has a toolbox of statistical and methodological procedures for those days when the numbers don’t turn out quite right.” Do you think every psychologist uses that toolbox? In other words, is everyone at least a little bit dirty?

Stapel: In essence, yes. The universe doesn’t give answers. There are no data matrices out there. We have to select from reality, and we have to interpret. There’s always dirt, and there’s always selection, and there’s always interpretation. That doesn’t mean it’s all untruthful. We’re dirty because we can only live with models of reality rather than reality itself. It doesn’t mean it’s all a bag of tricks and lies. But that’s where the inconvenience starts. Continue reading

Categories: junk science, Statistics | 11 Comments


3 years ago...
3 years ago…

MONTHLY MEMORY LANE: 3 years ago: June 2012. I mark in red three posts that seem most apt for general background on key issues in this blog.[1]  It was extremely difficult to pick only 3 this month; please check out others that look interesting to you. This new feature, appearing the last week of each month, began at the blog’s 3-year anniversary in Sept, 2014.


June 2012

[1]excluding those recently reblogged. Posts that are part of a “unit” or a group of “U-Phils” count as one.

Categories: 3-year memory lane | 1 Comment

Can You change Your Bayesian prior? (ii)



This is one of the questions high on the “To Do” list I’ve been keeping for this blog.  The question grew out of discussions of “updating and downdating” in relation to papers by Stephen Senn (2011) and Andrew Gelman (2011) in Rationality, Markets, and Morals.[i]

“As an exercise in mathematics [computing a posterior based on the client’s prior probabilities] is not superior to showing the client the data, eliciting a posterior distribution and then calculating the prior distribution; as an exercise in inference Bayesian updating does not appear to have greater claims than ‘downdating’.” (Senn, 2011, p. 59)

“If you could really express your uncertainty as a prior distribution, then you could just as well observe data and directly write your subjective posterior distribution, and there would be no need for statistical analysis at all.” (Gelman, 2011, p. 77)

But if uncertainty is not expressible as a prior, then a major lynchpin for Bayesian updating seems questionable. If you can go from the posterior to the prior, on the other hand, perhaps it can also lead you to come back and change it.

Is it legitimate to change one’s prior based on the data?

I don’t mean update it, but reject the one you had and replace it with another. My question may yield different answers depending on the particular Bayesian view. I am prepared to restrict the entire question of changing priors to Bayesian “probabilisms”, meaning the inference takes the form of updating priors to yield posteriors, or to report a comparative Bayes factor. Interpretations can vary. In many Bayesian accounts the prior probability distribution is a way of introducing prior beliefs into the analysis (as with subjective Bayesians) or, conversely, to avoid introducing prior beliefs (as with reference or conventional priors). Empirical Bayesians employ frequentist priors based on similar studies or well established theory. There are many other variants.



S. SENN: According to Senn, one test of whether an approach is Bayesian is that while Continue reading

Categories: Bayesian/frequentist, Gelman, S. Senn, Statistics | 111 Comments

Some statistical dirty laundry: The Tilberg (Stapel) Report on “Flawed Science”

Objectivity 1: Will the Real Junk Science Please Stand Up?


I had a chance to reread the 2012 Tilberg Report* on “Flawed Science” last night. The full report is now here. The discussion of the statistics is around pp. 17-21 (of course there was so little actual data in this case!) You might find it interesting. Here are some stray thoughts reblogged from 2 years ago…

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: Continue reading

Categories: junk science, spurious p values | 14 Comments

Evidence can only strengthen a prior belief in low data veracity, N. Liberman & M. Denzler: “Response”



I thought the criticisms of social psychologist Jens Förster were already quite damning (despite some attempts to explain them as mere QRPs), but there’s recently been some pushback from two of his co-authors Liberman and Denzler. Their objections are directed to the application of a distinct method, touted as “Bayesian forensics”, to their joint work with Förster. I discussed it very briefly in a recent “rejected post“. Perhaps the earlier method of criticism was inapplicable to these additional papers, and there’s an interest in seeing those papers retracted as well as the one that was. I don’t claim to know. A distinct “policy” issue is whether there should be uniform standards for retraction calls. At the very least, one would think new methods should be well-vetted before subjecting authors to their indictment (particularly methods which are incapable of issuing in exculpatory evidence, like this one). Here’s a portion of their response. I don’t claim to be up on this case, but I’d be very glad to have reader feedback.

Nira Liberman, School of Psychological Sciences, Tel Aviv University, Israel

Markus Denzler, Federal University of Applied Administrative Sciences, Germany

June 7, 2015

Response to a Report Published by the University of Amsterdam

The University of Amsterdam (UvA) has recently announced the completion of a report that summarizes an examination of all the empirical articles by Jens Förster (JF) during the years of his affiliation with UvA, including those co-authored by us. The report is available online. The report relies solely on statistical evaluation, using the method originally employed in the anonymous complaint against JF, as well as a new version of a method for detecting “low scientific veracity” of data, developed by Prof. Klaassen (2015). The report concludes that some of the examined publications show “strong statistical evidence for low scientific veracity”, some show “inconclusive evidence for low scientific veracity”, and some show “no evidence for low veracity”. UvA announced that on the basis of that report, it would send letters to the Journals, asking them to retract articles from the first category, and to consider retraction of articles in the second category.

After examining the report, we have reached the conclusion that it is misleading, biased and is based on erroneous statistical procedures. In view of that we surmise that it does not present reliable evidence for “low scientific veracity”.

We ask you to consider our criticism of the methods used in UvA’s report and the procedures leading to their recommendations in your decision.

Let us emphasize that we never fabricated or manipulated data, nor have we ever witnessed such behavior on the part of Jens Förster or other co-authors.

Here are our major points of criticism. Please note that, due to time considerations, our examination and criticism focus on papers co-authored by us. Below, we provide some background information and then elaborate on these points. Continue reading

Categories: junk science, reproducibility | Tags: | 9 Comments

“Fraudulent until proved innocent: Is this really the new “Bayesian Forensics”? (rejected post)

Objectivity 1: Will the Real Junk Science Please Stand Up?Fraudulent until proved innocent: Is this really the new “Bayesian Forensics”? (rejected post)




Categories: evidence-based policy, frequentist/Bayesian, junk science, Rejected Posts | 2 Comments

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

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