Continuing with the discussion of E.S. Pearson in honor of his birthday: Egon Pearson’s Neglected Contributions to Statistics by Aris Spanos Egon Pearson (11 August 1895 – 12 June 1980), is widely known today for his contribution in recasting of Fisher’s significance testing into the Neyman-Pearson (1933) theory of hypothesis testing. Occasionally, he is also […]

## How feminism has made me a better scientist

Feminism is not a branch of science. It is not a set of testable propositions about the observable world, nor is it any single research method. From my own perspective, feminism is a political movement associated with successes such as votes for women, setbacks such as the failed Equal Rights Amendment, and continuing struggles in […]

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## “Usefully skeptical science journalism”

Dean Eckles writes: I like this Wired piece on the challenges of learning about how technologies are affecting us and children. The journalist introducing a nice analogy (that he had in mind before talking with me — I’m briefly quoted) between the challenges in nutrition (and observational epidemiology more generally) and in studying “addictive” technologies. […]

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## Egon Pearson’s Heresy

Today is Egon Pearson’s birthday. In honor of his birthday, I am posting “Statistical Concepts in Their Relation to Reality” (Pearson 1955). I’ve posted it several times over the years, but always find a new gem or two, despite its being so short. E. Pearson rejected some of the familiar tenets that have come to […]

## Discussion of the value of a mathematical model for the dissemination of propaganda

A couple people pointed me to this article, “How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks,” by James Weatherall, Cailin O’Connor, and Justin Bruner, also featured in this news article. Their paper begins: In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi Oreskes and Erik Conway describe […]

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## Jeremy Freese was ahead of the curve

Here’s sociologist Jeremy Freese writing, back in 2008: Key findings in quantitative social science are often interaction effects in which the estimated “effect” of a continuous variable on an outcome for one group is found to differ from the estimated effect for another group. An example I use when teaching is that the relationship between […]

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