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Archive of posts filed under the Miscellaneous Statistics category.

“99.60% for women and 99.58% for men, P < 0.05.”

Gur Huberman pointed me to this paper by Tamar Kricheli-Katz and Tali Regev, “How many cents on the dollar? Women and men in product markets.” It appeared in something called ScienceAdvances, which seems to be some extension of the Science brand, i.e., it’s in the tabloids! I’ll leave the critical analysis of this paper to […]

Now that’s what I call a power pose!

John writes: See below for your humour file or blogging on a quiet day. . . . Perhaps you could start a competition for the wackiest real-life mangling of statistical concepts (restricted to a genuine academic setting?). On 15 Feb 2016, at 5:25 PM, [****] wrote: Pick of the bunch from tomorrow’s pile of applications […]

Happy talk, meet the Edlin factor

Mark Palko points us to this op-ed in which psychiatrist Richard Friedman writes: There are also easy and powerful ways to enhance learning in young people. For example, there is intriguing evidence that the attitude that young people have about their own intelligence — and what their teachers believe — can have a big impact […]

“Null hypothesis” = “A specific random number generator”

In an otherwise pointless comment thread the other day, Dan Lakeland contributed the following gem: A p-value is the probability of seeing data as extreme or more extreme than the result, under the assumption that the result was produced by a specific random number generator (called the null hypothesis). I could care less about p-values […]

Gary Venter’s age-period-cohort decomposition of US male mortality trends

Following up on yesterday’s post on mortality trends, I wanted to share with you a research note by actuary Gary Venter, “A Quick Look at Cohort Effects in US Male Mortality.” Venter produces this graph: And he writes: Cohort effects in mortality tend to be difficult to explain. Often strings of coincidences are invoked – […]

If Yogi Berra could see this one, he’d spin in his grave: Regression modeling using a convenience sample

Kelvin Leshabari writes: We are currently planning to publish some few manuscripts on the outcome of treatment of some selected cancers occuring in children. The current dataset was derived from the natural admission process of those children with cancer found at a selected tertiary cancer centre. To the best of our understanding, our data are […]

“Cancer Research Is Broken”

Michael Oakes pointed me to this excellent news article by Daniel Engber, subtitled, “There’s a replication crisis in biomedicine—and no one even knows how deep it runs.” Engber suggests that the replication problem in biomedical research is worse than the much-publicized replication problem in psychology. One reason, which I didn’t see Engber discussing, is financial […]

“if you add a few more variables, you can do a better job at predictions”

Ethan Bolker points me to this news article by Neil Irwin: Robert J. Gordon, an economist at Northwestern University, has his own version that he argues explains inflation levels throughout recent decades. But it is hardly simple. Its prediction for inflation relies not just on joblessness but also on measures of productivity growth, six shifts […]

Black Box Challenge

Georgy Cheremovskiy writes: I’m one of the organizers of an unusual reinforcement learning competition named Black Box Challenge. The conception is simple — one need to program an agent that can play a game with unknown rules. At each time step agent is given an environment state vector and has a few possible actions. The […]

These celebrity photos are incredible: Type S errors in use!

Kaveh sends along this, from a recent talk at Berkeley by Katherine Casey: It’s so gratifying to see this sort of thing in common use, only 15 years after Francis and I introduced the idea (and see also this more recent paper with Carlin).

Best Disclaimer Ever

Paul Alper sends this in, from the article, “Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial,” by Ian J Jacobs, Usha Menon, Andy Ryan, Aleksandra Gentry-Maharaj, Matthew Burnell, Jatinderpal K Kalsi, Nazar N Amso, Sophia Apostolidou, Elizabeth Benjamin, Derek Cruickshank, Danielle N Crump, Susan […]

A question about software for an online survey

Michael Smith writes: I have a research challenge and I was hoping you could spare a minute of your time. I hope it isn’t a bother—I first came across you when I saw your post on how psychology researchers can learn from statisticians. I figure even if you don’t know the answer to this question, […]

He does mathematical modeling and is asking for career advice: wants to move from biology to social science

Rick Desper writes: I face some tough career choices. I have a background in mathematical modeling (got my Ph.D. in math from Rutgers back in the late ’90s) and spent several years working in the field of bioinformatics/computational biology (its name varies from place to place). I’ve worked on problems in modeling cancer progression and […]

Stan Case Studies Launches

There’s a new section of the Stan web site, with case studies meant to illustrate statistical methodologies, classes of models, application areas, statistical computation, and Stan programming. Stan Case Studies The first ten or so are up, including a grab bag of education models from Daniel Furr at U.C. Berkeley: Hierarchical Two-Parameter Logistic Item Response […]

Swimsuit special: “A pure Bayesian or pure non-Bayesian is not forever doomed to use out-of-date methods, but at any given time the purist will be missing some of the most effective current techniques.”

Joshua Vogelstein points me to this paper by Gerd Gigerenzer and Julian Marewski, who write: The idol of a universal method for scientific inference has been worshipped since the “inference revolution” of the 1950s. Because no such method has ever been found, surrogates have been created, most notably the quest for significant p values. This […]

Statistics is like basketball, or knitting

I had a recent exchange with a news reporter regarding one of those silly psychology studies. I took a look at the article in question—this time it wasn’t published in Psychological Science or PPNAS so it didn’t get saturation publicity—and indeed it was bad, laughably bad. They didn’t just have the garden of forking paths, […]

Good advice can do you bad

Here are some examples of good, solid, reasonable statistical advice which can lead people astray. Example 1 Good advice: Statistical significance is not the same as practical significance. How it can mislead: People get the impression that a statistically significant result is more impressive if it’s larger in magnitude. Why it’s misleading: See this classic […]

Le Menu Dit : a translation app

This post is by Phil Price. “Le Menu Dit” is an iPhone app that some friends and I wrote, which translates restaurant menus from English into French. (The name is French for “The Menu Says.”) The friends are Nathan Addy and another excellent programmer who would like to remain nameless for now. Here’s how the […]

Bruised and battered, I couldn’t tell what I felt. I was ungeneralizable to myself.

One more rep. The new thing you just have to read, if you’re following the recent back-and-forth on replication in psychology, is this post at Retraction Watch in which Nosek et al. respond to criticisms from Gilbert et al. regarding the famous replication project. Gilbert et al. claimed that many of the replications in the […]

The problems with p-values are not just with p-values: My comments on the recent ASA statement

The American Statistical Association just released a committee report on the use of p-values. I was one of the members of the committee but I did not write the report. We were also given the opportunity to add our comments. Here’s what I sent: