Professor Richard Gill
It was statistician Richard Gill who first told me about Diederik Stapel (see an earlier post on Diederik). We were at a workshop on Error in the Sciences at Leiden in 2011. I was very lucky to have Gill be assigned as my commentator/presenter—he was excellent! As I was explaining some data problems to him, he suddenly said, “Some people don’t bother to collect data at all!” That’s when I learned about Stapel.
Committees often turn to Gill when someone’s work is up for scrutiny of bad statistics or fraud, or anything in between. Do you think he’s being too easy on researchers when he says, about a given case:
“data has been obtained by some combination of the usual ‘questionable research practices’ [QRPs] which are prevalent in the field in question. 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.”
Isn’t that the danger in relying on deeply felt background beliefs? Have our attitudes changed (toward QRPs) over the past 3 years (harsher or less harsh)? Here’s a talk of his I blogged 3 years ago (followed by a letter he allowed me to post). I reflect on the pseudoscientific nature of the ‘recovered memories’ program in one of the Geraerts et al. papers in a later post. Continue reading
For entertainment only
In a post 3 years ago (“What do these share in common: m&m’s, limbo stick, ovulation, Dale Carnegie? Sat night potpourri”), I expressed doubts about expending serious effort to debunk the statistical credentials of studies that most readers without any statistical training would regard as “for entertainment only,” dubious, or pseudoscientific quackery. It needn’t even be that the claim is implausible, what’s implausible is that it has been well probed in the experiment at hand. Given the attention being paid to such examples by some leading statisticians, and scores of replication researchers over the past 3 years–attention that has been mostly worthwhile–maybe the bar has been lowered. What do you think? Anyway, this is what I blogged 3 years ago. (Oh, I decided to put in a home-made cartoon!) Continue reading
Right after our session at the SPSP meeting last Friday, I chaired a symposium on replication that included Brian Earp–an active player in replication research in psychology (Replication and Evidence: A tenuous relationship p. 80). One of the first things he said, according to my notes, is that gambits such as cherry picking, p-hacking, hunting for significance, selective reporting, and other QRPs, had
been taught as acceptable become standard practice in psychology, without any special need to adjust p-values or alert the reader to their spuriousness [i]. (He will correct me if I’m wrong.) It shocked me to hear it, even though it shouldn’t have, given what I’ve learned about statistical practice in social science. It was the Report on Stapel that really pulled back the curtain on this attitude toward QRPs in social psychology–as discussed in this blogpost 3 years ago. (If you haven’t read Section 5 of the report on flawed science, you should.) Many of us assumed that QRPs, even if still committed, were at least recognized to be bad statistical practices since the time of Morrison and Henkel’s (1970) Significance Test Controversy. A question now is this: have all the confessions of dirty laundry, the fraudbusting of prominent researchers, the pledges to straighten up and fly right, the years of replication research, done anything to remove the stains? I leave the question open for now. Here’s my “statistical dirty laundry” post from 2013: Continue reading
Here are the slides from our talk at the Society for Philosophy of Science in Practice (SPSP) conference. I covered the first 27, Parker the rest. The abstract is here:
I’m giving a joint presentation with Caitlin Parker on Friday (June 17) at the meeting of the Society for Philosophy of Science in Practice (SPSP): “Using Philosophy of Statistics to Make Progress in the Replication Crisis in Psychology” (Rowan University, Glassboro, N.J.) The Society grew out of a felt need to break out of the sterile straightjacket wherein philosophy of science occurs divorced from practice. The topic of the relevance of PhilSci and PhilStat to Sci has often come up on this blog, so people might be interested in the SPSP mission statement below our abstract.
Using Philosophy of Statistics to Make Progress in the Replication Crisis in Psychology
Deborah Mayo Virginia Tech, Department of Philosophy United States
Caitlin Parker Virginia Tech, Department of Philosophy United States
I came across an excellent post on a blog kept by Daniel Lakens: “So you banned p-values, how’s that working out for you?” He refers to the journal that recently banned significance tests, confidence intervals, and a vague assortment of other statistical methods, on the grounds that all such statistical inference tools are “invalid” since they don’t provide posterior probabilities of some sort (see my post). The editors’ charge of “invalidity” could only hold water if these error statistical methods purport to provide posteriors based on priors, which is false. The entire methodology is based on methods in which probabilities arise to qualify the method’s capabilities to detect and avoid erroneous interpretations of data . The logic is of the falsification variety found throughout science. Lakens, an experimental psychologist, does a great job delineating some of the untoward consequences of their inferential ban. I insert some remarks in black. Continue reading
Winner of the May 2016 Palindrome contest
Curtis Williams: Inventor, entrepreneur, and professional actor
The winning palindrome (a dialog):
“Disable preplan?… I, Mon Ami?”
“No! I tag. I vandalized Dezi, lad.”
“Navigational leftism lacks aim…a nominal perp: Elba’s id.”
The requirement: A palindrome using “navigate” or “navigation” (and Elba, of course).
Book choice: Error and Inference (D. Mayo & A. Spanos, Cambridge University Press, 2010)
Bio: Curtis Mark Williams is the co-founder of WavHello and the inventor of Bellybuds, who also counts himself as an occasional professional actor who has performed on Broadway  and in several television shows and films.
He currently resides in Los Angeles with his lovely wife, two daughters, his dog, Newton, and his framed New Yorker Caption Contest winning cartoon. [He has been a finalist twice and the one he won is contest #329, by Joe Dator (inspired by his theatrical background. :)] Continue reading
Aris Spanos, my colleague (in economics) and co-author, came across this anonymous review of our Error and Inference (2010) [E & I]. Interestingly, the reviewer remarks that “The book gives a sense of security regarding the future of statistical science and its importance in many walks of life.” We’re not sure what the reviewer means–but it’s appreciated regardless. This post was from yesterday’s 3-year memory lane and was first posted here.
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. Continue reading