Category: Miscellaneous Statistics

Ethics in statistical practice and communication: Five recommendations.

I recently published an article summarizing some of my ideas on ethics in statistics, going over these recommendations: 1. Open data and open methods, 2. Be clear about the information that goes into statistical procedures, 3. Create a culture of respect for data, 4. Publication of criticisms, 5. Respect the limitations of statistics. The full […]

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Toward better measurement in K-12 education research

Billy Buchanan, Director of Data, Research, and Accountability, Fayette County Public Schools, Lexington, Kentucky, expresses frustration with the disconnect between the large and important goals of education research, on one hand, and the gaps in measurement and statistical training, on the other. Buchanan writes: I don’t think that every classroom educator, instructional coach, principal, or […]

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“Ivy League Football Saw Large Reduction in Concussions After New Kickoff Rules”

I noticed this article in the newspaper today: A simple rule change in Ivy League football games has led to a significant drop in concussions, a study released this week found. After the Ivy League changed its kickoff rules in 2016, adjusting the kickoff and touchback lines by just five yards, the rate of concussions […]

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My talk tomorrow (Tues) 4pm in the Biomedical Informatics department (at 168th St)

The talk is 4-5pm in Room 200 on the 20th floor of the Presbyterian Hospital Building, Columbia University Medical Center. I’m not sure what I’m gonna talk about. It’ll depend on what people are interested in discussing. Here are some possible topics: – The failure of null hypothesis significance testing when studying incremental changes, and […]

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(People are missing the point on Wansink, so) what’s the lesson we should be drawing from this story?

People pointed me to various recent news articles on the retirement from the Cornell University business school of eating-behavior researcher and retraction king Brian Wansink. I particularly liked this article by David Randall—not because he quoted me, but because he crisply laid out the key issues: The irreproducibility crisis cost Brian Wansink his job. Over […]

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Job opening at CDC: “The Statistician will play a central role in guiding the statistical methods of all major projects of the Epidemiology and Prevention Branch of the CDC Influenza Division, and aid in designing, analyzing, and interpreting research intended to understand the burden of influenza in the US and internationally and identify the best influenza vaccines and vaccine strategies.”

This sounds super interesting: Vacancy Information: Mathematical Statistician, GS-1529-14 Please apply at one of the following: · DE (External candidates to the US GOV) Announcement: HHS-CDC-D3-18-10312897 · MP (Internal candidates to the US GOV) Announcement: HHS-CDC-M3-18-10312898 Location: Atlanta, GA – Centers for Disease Control and Prevention – National Center for Immunization and Respiratory Disease – […]

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Don’t calculate post-hoc power using observed estimate of effect size

Aleksi Reito writes: The statement below was included in a recent issue of Annals of Surgery: But, as 80% power is difficult to achieve in surgical studies, we argue that the CONSORT and STROBE guidelines should be modified to include the disclosure of power—even if less than 80%—with the given sample size and effect size […]

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“Tweeking”: The big problem is not where you think it is.

In her recent article about pizzagate, Stephanie Lee included this hilarious email from Brian Wansink, the self-styled “world-renowned eating behavior expert for over 25 years”: OK, what grabs your attention is that last bit about “tweeking” the data to manipulate the p-value, where Wansink is proposing research misconduct (from NIH: “Falsification: Manipulating research materials, equipment, […]

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A psychology researcher uses Stan, multiverse, and open data exploration to explore human memory

Under the heading, “An example of Stan to the rescue, multiverse analysis, and psychologists trying to do well,” Greg Cox writes: I’m currently a postdoc at Syracuse University studying how human memory works. I wanted to forward a paper of ours [“Information and Processes Underlying Semantic and Episodic Memory Across Tasks, Items, and Individuals,” by […]

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Columbia Data Science Institute art contest

This is a great idea! Unfortunately, only students at Columbia can submit. I encourage other institutions to do such contests too. We did something similar at Columbia, maybe 10 or 15 years ago? It went well, we just didn’t have the energy to do it again every year, as we’d initially planned. So I’m very […]

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High-profile statistical errors occur in the physical sciences too, it’s not just a problem in social science.

In an email with subject line, “Article full of forking paths,” John Williams writes: I thought you might be interested in this article by John Sabo et al., which was the cover article for the Dec. 8 issue of Science. The article is dumb in various ways, some of which are described in the technical […]

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Bothered by non-monotonicity? Here’s ONE QUICK TRICK to make you happy.

We’re often modeling non-monotonic functions. For example, performance at just about any task increases with age (babies can’t do much!) and then eventually decreases (dead people can’t do much either!). Here’s an example from a few years ago: A function g(x) that increases and then decreases can be modeled by a quadratic, or some more […]

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The gaps between 1, 2, and 3 are just too large.

Someone who wishes to remain anonymous points to a new study of David Yeager et al. on educational mindset interventions (link from Alex Tabarrok) and asks: On the blog we talk a lot about bad practice and what not to do. Might this be an example of how *to do* things? Or did they just […]

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Some clues that this study has big big problems

Paul Alper writes: This article from the New York Daily News, reproduced in the Minneapolis Star Tribune, is so terrible in so many ways. Very sad commentary regarding all aspects of statistics education and journalism. The news article, by Joe Dziemianowicz, is called “Study says drinking alcohol is key to living past 90,” with subheading, […]

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“To get started, I suggest coming up with a simple but reasonable model for missingness, then simulate fake complete data followed by a fake missingness pattern, and check that you can recover your missing-data model and your complete data model in that fake-data situation. You can then proceed from there. But if you can’t even do it with fake data, you’re sunk.”

Alex Konkel writes on a topic that never goes out of style: I’m working on a data analysis plan and am hoping you might help clarify something you wrote regarding missing data. I’m somewhat familiar with multiple imputation and some of the available methods, and I’m also becoming more familiar with Bayesian modeling like in […]

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In statistics, we talk about uncertainty without it being viewed as undesirable

Lauren Kennedy writes: I’ve noticed that statistics (or at least applied statistics) has this nice ability to talk about uncertainty without it being viewed as undesirable. Stan Con had that atmosphere and I think it just makes everyone so much more willing to debug, discuss and generate new ideas. Indeed, in statistics I’ve seen fierce […]

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Data concerns when interpreting comparisons of gender equality between countries

A journalist pointed me to this research article, “Gender equality and sex differences in personality: evidence from a large, multi-national sample,” by Tim Kaiser, which reports: A large, multinational (N = 926,383) dataset was used to examine sex differences in Big Five facet scores for 70 countries. Difference scores were aggregated to a multivariate effect […]

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Data concerns when interpreting comparisons of gender equality between countries

A journalist pointed me to this research article, “Gender equality and sex differences in personality: evidence from a large, multi-national sample,” by Tim Kaiser (see also news report by Angela Lashbrook here), which states: A large, multinational (N = 926,383) dataset was used to examine sex differences in Big Five facet scores for 70 countries. […]

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No, I don’t think it’s the file drawer effect

Someone named Andrew Certain writes: I’ve been reading your blog since your appearance on Econtalk . . . explaining the ways in which statistics are misused/misinterpreted in low-sample/high-noise studies. . . . I recently came across a meta-analysis on stereotype threat [a reanalysis by Emil Kirkegaard] by that identified a clear relationship between smaller sample […]

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