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Archive of posts filed under the Miscellaneous Statistics category.

Problems with the jargon “statistically significant” and “clinically significant”

Someone writes: After listening to your EconTalk episode a few weeks ago, I have a question about interpreting treatment effect magnitudes, effect sizes, SDs, etc. I studied Econ/Math undergrad and worked at a social science research institution in health policy as a research assistant, so I have a good amount of background. At the institution […]

Ride a Crooked Mile

Joachim Krueger writes: As many of us rely (in part) on p values when trying to make sense of the data, I am sending a link to a paper Patrick Heck and I published in Frontiers in Psychology. The goal of this work is not to fan the flames of the already overheated debate, but […]

Criminology corner: Type M error might explain Weisburd’s Paradox

[silly cartoon found by googling *cat burglar*] Torbjørn Skardhamar, Mikko Aaltonen, and I wrote this article to appear in the Journal of Quantitative Criminology: Simple calculations seem to show that larger studies should have higher statistical power, but empirical meta-analyses of published work in criminology have found zero or weak correlations between sample size and […]

Why I’m not participating in the Transparent Psi Project

I received the following email from psychology researcher Zoltan Kekecs: I would like to ask you to participate in the establishment of the expert consensus design of a large scale fully transparent replication of Bem’s (2011) ‘Feeling the future’ Experiment 1. Our initiative is called the ‘Transparent Psi Project’. [https://osf.io/jk2zf/wiki/home/] Our aim is to develop […]

Financial anomalies are contingent on being unknown

Jonathan Falk points us to this article by Kewei Hou, Chen Xue, and Lu Zhang, who write: In retrospect, the anomalies literature is a prime target for p-hacking. First, for decades, the literature is purely empirical in nature, with little theoretical guidance. Second, with trillions of dollars invested in anomalies-based strategies in the U.S.market alone, […]

“Bombshell” statistical evidence for research misconduct, and what to do about it?

Someone pointed me to this post by Nick Brown discussing a recent article by John Carlisle regarding scientific misconduct. Here’s Brown: [Carlisle] claims that he has found statistical evidence that a surprisingly high proportion of randomised controlled trials (RCTs) contain data patterns that cannot have arisen by chance. . . . the implication is that […]

No conf intervals? No problem (if you got replication).

This came up in a research discussion the other day. Someone had produced some estimates, and there was a question: where are the conf intervals. I said that if you have replication and you graph the estimates that were produced, then you don’t really need conf intervals (or, for that matter, p-values). The idea is […]

How has my advice to psychology researchers changed since 2013?

Four years ago, in a post entitled, “How can statisticians help psychologists do their research better?”, I gave the following recommendations to researchers: – Analyze all your data. – Present all your comparisons. – Make your data public. And, for journal editors, I wrote, “if a paper is nothing special, you don’t have to publish […]

Static sensitivity analysis

After this discussion, I pointed Ryan Giordano, Tamara Broderick, and Michael Jordan to Figure 4 of this paper with Bois and Jiang as an example of “static sensitivity analysis.” I’ve never really followed up on this idea but I think it could be useful for many problems. Giordano replied: Here’s a copy of Basu’s robustness […]

This company wants to hire people who can program in R or Python and do statistical modeling in Stan

Doug Puett writes: I am a 2012 QMSS [Columbia University Quantitative Methods in Social Sciences] grad who is currently trying to build a Data Science/Quantitative UX team, and was hoping for some advice. I am finding myself having a hard time finding people who are really interested in understanding people and who especially are excited […]

Some natural solutions to the p-value communication problem—and why they won’t work.

John Carlin and I write: It is well known that even experienced scientists routinely misinterpret p-values in all sorts of ways, including confusion of statistical and practical significance, treating non-rejection as acceptance of the null hypothesis, and interpreting the p-value as some sort of replication probability or as the posterior probability that the null hypothesis […]

#NotAll4YearOlds

I think there’s something wrong this op-ed by developmental psychologist Alison Gopnik, “4-year-olds don’t act like Trump,” and which begins, The analogy is pervasive among his critics: Donald Trump is like a child. . . . But the analogy is profoundly wrong, and it’s unfair to children. The scientific developmental research of the past 30 […]

Hotel room aliases of the statisticians

Barry Petchesky writes: Below you’ll find a room list found before Game 1 at the Four Seasons in Houston (right across from the arena), where the Thunder were staying for their first-round series against the Rockets. We didn’t run it then because we didn’t want Rockets fans pulling the fire alarm or making late-night calls […]

Taking Data Journalism Seriously

This is a bit of a followup to our recent review of “Everybody Lies.” While writing the review I searched the blog for mentions of Seth Stephens-Davidowitz, and I came across this post from last year, concerning a claim made by author J. D. Vance that “the middle part of America is more religious than […]

Accounting for variation and uncertainty

[cat picture] Yesterday I gave a list of the questions they’re asking me when I speak at the Journal of Accounting Research Conference. All kidding aside, I think that a conference of accountants is the perfect setting for a discussion of of research integrity, as accounting is all about setting up institutions to enable trust. […]

A completely reasonable-sounding statement with which I strongly disagree

From a couple years ago: In the context of a listserv discussion about replication in psychology experiments, someone wrote: The current best estimate of the effect size is somewhere in between the original study and the replication’s reported value. This conciliatory, split-the-difference statement sounds reasonable, and it might well represent good politics in the context […]

7th graders trained to avoid Pizzagate-style data exploration—but is the training too rigid?

[cat picture] Laura Kapitula writes: I wanted to share a cute story that gave me a bit of hope. My daughter who is in 7th grade was doing her science project. She had designed an experiment comparing lemon batteries to potato batteries, a 2×4 design with lemons or potatoes as one factor and number of […]

What hypothesis testing is all about. (Hint: It’s not what you think.)

From 2015: The conventional view: Hyp testing is all about rejection. The idea is that if you reject the null hyp at the 5% level, you have a win, you have learned that a certain null model is false and science has progressed, either in the glamorous “scientific revolution” sense that you’ve rejected a central […]

The statistical crisis in science: How is it relevant to clinical neuropsychology?

[cat picture] Hilde Geurts and I write: There is currently increased attention to the statistical (and replication) crisis in science. Biomedicine and social psychology have been at the heart of this crisis, but similar problems are evident in a wide range of fields. We discuss three examples of replication challenges from the field of social […]

The Bolt from the Blue

Lionel Hertzog writes: In the method section of a recent Nature article in my field of research (diversity-ecosystem function) one can read the following: The inclusion of many predictors in statistical models increases the chance of type I error (false positives). To account for this we used a Bernoulli process to detect false discovery rates, […]