Category: Statistics

“A Headline That Will Make Global-Warming Activists Apoplectic”

I saw this article in the newspaper today, “2017 Was One of the Hottest Years on Record. And That Was Without El Niño,” subtitled, “The world in 2017 saw some of the highest average surface temperatures ever recorded, surprising scientists who had expected sharper retreat from recent record years,” and accompanied by the above graph, […]

The post “A Headline That Will Make Global-Warming Activists Apoplectic” appeared first on Statistical Modeling, Causal Inference, and Social Science.

Stan short course in NYC in 2.5 weeks

To all who may be interested:
Jonah Gabry, Stan developer and creator of ShinyStan, will be giving a short course downtown, from 6-8 Aug. Details here.
Jonah has taught Stan courses before, and he knows what he’s doing.
The post Stan short cours…

“The idea of replication is central not just to scientific practice but also to formal statistics . . . Frequentist statistics relies on the reference set of repeated experiments, and Bayesian statistics relies on the prior distribution which represents the population of effects.”

Rolf Zwaan (who we last encountered here in “From zero to Ted talk in 18 simple steps”), Alexander Etz, Richard Lucas, and M. Brent Donnellan wrote an article, “Making replication mainstream,” which begins: Many philosophers of science and methodologists have argued that the ability to repeat studies and obtain similar results is an essential component […]

The post “The idea of replication is central not just to scientific practice but also to formal statistics . . . Frequentist statistics relies on the reference set of repeated experiments, and Bayesian statistics relies on the prior distribution which represents the population of effects.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

If you have a measure, it will be gamed (politics edition).

They sometimes call it Campbell’s Law: New York Governor Andrew Cuomo is not exactly known for drumming up grassroots enthusiasm and small donor contributions, so it was quite a surprise on Monday when his reelection campaign reported that more than half of his campaign contributors this year gave $250 or less. But wait—a closer examination […]

The post If you have a measure, it will be gamed (politics edition). appeared first on Statistical Modeling, Causal Inference, and Social Science.

“For professional baseball players, faster hand-eye coordination linked to batting performance”

Kevin Lewis sends along this press release reporting what may be the least surprising laboratory finding since the classic “Participants reported being hungrier when they walked into the café (mean = 7.38, SD = 2.20) than when they walked out [mean = 1.53, SD = 2.70, F(1, 75) = 107.68, P

The post “For professional baseball players, faster hand-eye coordination linked to batting performance” appeared first on Statistical Modeling, Causal Inference, and Social Science.

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 […]

The post The statistical checklist: Could there be a list of guidelines to help analysts do better work? appeared first on Statistical Modeling, Causal Inference, and Social Science.

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 […]

The post The “Carl Sagan effect” appeared first on Statistical Modeling, Causal Inference, and Social Science.

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 […]

The post Mister P wins again appeared first on Statistical Modeling, Causal Inference, and Social Science.