Kevin Lewis sends along this article by Laura Pritschet, Derek Powell, and Zachary Horne, who write: Some effects are statistically significant. Other effects do not reach the threshold of statistical significance and are sometimes described as “marginally significant” or as “approaching significance.” Although the concept of marginal significance is widely deployed in academic psychology, there […]
The post “Marginally Significant Effects as Evidence for Hypotheses: Changing Attitudes Over Four Decades” appeared first on Statistical Modeling, Causal Inference, and Social Science.

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Academics / Mathematics : Statistical Modeling, Causal Inference, and Social Science

Commenter Rahul asked what I thought of this note by Scott Firestone (link from Tyler Cowen) criticizing a recent discussion by Kevin Drum suggesting that lead exposure causes violent crime. Firestone writes: It turns out there was ...

Academics / Mathematics : Statistical Modeling, Causal Inference, and Social Science

Florian Wickelmaier writes: I’m writing to tell you about my experiences with another instance of “the difference between significant and not significant.” In a lab course, I came across a paper by Costa et al. [Cognition 130 (2) (2...

Academics / Mathematics : Statistical Modeling, Causal Inference, and Social Science

You’ve heard it a million times, the idea is that if you have an estimate of.003 (on some reasonable scale in which 1 is a meaningful effect size) and a standard error of.001 then, yes, the estimate is statistically significant but ...

Academics / Mathematics : Statistical Modeling, Causal Inference, and Social Science

As D.M.C. would say, bad meaning bad not bad meaning good. Deborah Mayo points to this terrible, terrible definition of statistical significance from the Agency for Healthcare Research and Quality: Statistical Significance Definitio...

Academics / Mathematics : Statistical Modeling, Causal Inference, and Social Science

Dean Eckles writes: Thought you might be interested in an example that touches on a couple recurring topics: 1. The difference between a statistically significant finding and one that is non-significant need not be itself statistica...

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