Author: Andrew

The statistical checklist: Could there be a list of guidelines to help analysts do better work?

[image of cat with a checklist] Paul Cuffe writes: Your idea of “researcher degrees of freedom” [actually not my idea; the phrase comes from Simmons, Nelson, and Simonsohn] really resonates with me: I’m continually surprised by how many researchers freestyle their way through a statistical analysis, using whatever tests, and presenting whatever results, strikes their […]

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The “Carl Sagan effect”

Javier Benítez writes: I am not in academia, but I have learned a lot about science from what’s available to the public. But I also didn’t know that public outreach is looked down upon by academia. See the Carl Sagan Effect. Susana Martinez-Conde writes: One scientist, who agreed to participate on the condition of anonymity—an […]

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Mister P wins again

Chad Kiewiet De Jonge, Gary Langer, and Sofi Sinozich write: This paper presents state-level estimates of the 2016 presidential election using data from the ABC News/Washington Post tracking poll and multilevel regression with poststratification (MRP). While previous implementations of MRP for election forecasting have relied on data from prior elections to establish poststratification targets for […]

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The course of science

Shravan Vasishth sends this along:

Yup. Not always, though. Even though the above behavior is rewarded.
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What happens to your career when you have to retract a paper?

In response to our recent post on retractions, Josh Krieger sends along two papers he worked on with Pierre Azoulay, Jeff Furman, Fiona Murray, and Alessandro Bonatti. Krieger writes, “Both papers are about the spillover effects of retractions on other work. Turns out retractions are great for identification!” Paper #1: “The career effects of scandal: […]

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“Bayesian Meta-Analysis with Weakly Informative Prior Distributions”

Donny Williams sends along this paper, with Philippe Rast and Paul-Christian Bürkner, and writes: This paper is similar to the Chung et al. avoiding boundary estimates papers (here and here), but we use fully Bayesian methods, and specifically the half-Cauchy prior. We show it has as good of performance as a fully informed prior based […]

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The persistence of bad reporting and the reluctance of people to criticize it

Mark Palko pointed to a bit of puff-piece journalism on the tech entrepreneur Elon Musk that was so extreme that it read as a possible parody, and I wrote, “it could just be as simple as that [author Neil] Strauss decided that a pure puff piece would give him access to write a future Musk […]

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Should the points in this scatterplot be binned?

Someone writes: Care to comment on this paper‘s Figure 4? I found it a bit misleading to do scatter plots after averaging over multiple individuals. Most scatter plots could be “improved” this way to make things look much cleaner than they are. People are already advertising the paper using this figure. The article, Genetic analysis […]

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He wants to model a proportion given some predictors that sum to 1

Joël Gombin writes: I’m wondering what your take would be on the following problem. I’d like to model a proportion (e.g., the share of the vote for a given party at some territorial level) in function of some compositional data (e.g., the sociodemographic makeup of the voting population), and this, in a multilevel fashion (allowing […]

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Joint inference or modular inference? Pierre Jacob, Lawrence Murray, Chris Holmes, Christian Robert discuss conditions on the strength and weaknesses of these choices

Pierre Jacob, Lawrence Murray, Chris Holmes, Christian Robert write: In modern applications, statisticians are faced with integrating heterogeneous data modalities relevant for an inference, prediction, or decision problem. In such circumstances, it is convenient to use a graphical model to represent the statistical dependencies, via a set of connected “modules”, each relating to a specific […]

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Divisibility in statistics: Where is it needed?

The basics of Bayesian inference is p(parameters|data) proportional to p(parameters)*p(data|parameters). And, for predictions, p(predictions|data) = integral_parameters p(predictions|parameters,data)*p(parameters|data). In these expressions (and the corresponding simpler versions for maximum likelihood), “parameters” and “data” are unitary objects. Yes, it can be helpful to think of the parameter objects as being a list or vector of individual parameters; and […]

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I think they use witchcraft

The following came in the email today: On Jul 7, 2018, at 12:58 PM, Submissions <submissions@**.co.in> wrote: Hello Dr. Andrew Gelman, I am Dr. ** [American-sounding name], Research Assistant for the ** Publishing Company contacting you with reference from our Editorial Board. Are you tired of publishing your Manuscript in useless journals and get no […]

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He wants to know what to read and what software to learn, to increase his ability to think about quantitative methods in social science

A law student writes: I aspire to become a quantitatively equipped/focused legal academic. Despite majoring in economics at college, I feel insufficiently confident in my statistical literacy. Given your publicly available work on learning basic statistical programming, I thought I would reach out to you and ask for advice on understanding modeling and causal inference […]

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All of Life is 6 to 5 Against

Donny Williams writes: I have a question I have been considering asking you for a while. The more I have learned about Bayesian methods, including regularly reading the journal Bayesian Analysis (preparing a submission here, actually!), etc., I have come to not only see that frequency properties are studied of Bayesian models, but it is […]

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Tutorial: The practical application of complicated statistical methods to fill up the scientific literature with confusing and irrelevant analyses

James Coyne pointed me with distress or annoyance to this new paper, “Tutorial: The Practical Application of Longitudinal Structural Equation Mediation Models in Clinical Trials,” by K. A. Goldsmith, D. P. MacKinnon, T. Chalder, P. D. White, M. Sharpe, and A. Pickles. This is the team behind the PACE trial for systemic exercise intolerance disease. […]

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On this 4th of July, let’s declare independence from “95%”

Plan your experiment, gather your data, do your inference for all effects and interactions of interest. When all is said and done, accept some level of uncertainty in your conclusions: you might not be 97.5% sure that the treatment effect is positive, but that’s fine. For one thing, decisions need to be made. You were […]

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PNAS forgets basic principles of game theory, thus dooming thousands of Bothans to the fate of Alderaan

Under the subject line, “I needed this information to make a go/no-go decision on my new Death Star,” Kevin Lewis points to this press release from a prestigious journal: Because versions of the below articles were previously posted online, PNAS is publishing the articles without embargo: Potential atmospheres around TRAPPIST-1 planets Simulations of stellar winds […]

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About that claim in the NYT that the immigration issue helped Hillary Clinton? The numbers don’t seem to add up.

Today I noticed an op-ed by two political scientists, Howard Lavine and Wendy Rahm, entitled, “What if Trump’s Nativism Actually Hurts Him?”: Contrary to received wisdom, however, the immigration issue did not play to Mr. Trump’s advantage nearly as much as commonly believed. According to our analysis of national survey data from the American National […]

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