Category: Decision Theory

What’s the p-value good for: I answer some questions.

Martin King writes: For a couple of decades (from about 1988 to 2006) I was employed as a support statistician, and became very interested in the p-value issue; hence my interest in your contribution to this debate. (I am not familiar with the p-value ‘reconciliation’ literature, as published after about 2005.) I would hugely appreciate […]

My talk at the Brookings Institution this Fri 11am

The replication crisis in science: Does it matter for policy? Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University I argue that policy analysts should care about the replication crisis for three reasons: (1) High-profile policy claims have been systematically exaggerated; (2) This has implications for how to conduct and interpret new […]

Econ corner: A rational reason (beyond the usual “risk aversion” or concave utility function) for wanting to minimize future uncertainty in a decision-making setting

Eric Rasmusen sends along a paper, Option Learning as a Reason for Firms to Be Averse to Idiosyncratic Risk, and writes: It tries to distinguish between two kinds of risk. The distinction is between uncertainty that the firm will learn about, and uncertainty that will be bumping the profit process around forever. It’s not the […]

Glenn Shafer tells us about the origins of “statistical significance”.

Shafer writes: It turns out that Francis Edgeworth, who introduced “significant” in statistics, and Karl Pearson, who popularized it in statistics, used it differently than we do. For Edgeworth and Pearson, “being significant” meant “signifying”. An observed difference was significant if it signified a real difference, and you needed a very small p-value to be […]

Deterministic thinking (“dichotomania”): a problem in how we think, not just in how we act

This has come up before: – Basketball Stats: Don’t model the probability of win, model the expected score differential. – Econometrics, political science, epidemiology, etc.: Don’t model the probability of a discrete outcome, model the underlying continuous variable – Thinking like a statistician (continuously) rather than like a civilian (discretely) – Message to Booleans: It’s […]

It’s not just p=0.048 vs. p=0.052

Peter Dorman points to this post on statistical significance and p-values by Timothy Taylor, editor of the Journal of Economic Perspectives, a highly influential publication of the American Economic Association. I have some problems with what Taylor writes, but for now I’ll just take it as representing a certain view, the perspective of a thoughtful […]

Calibration and sharpness?

I really liked this paper, and am curious what other people think before I base a grant application around applying Stan to this problem in a machine-learning context. Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(2), 243–268. Gneiting […]

Beyond Power Calculations: Some questions, some answers

Brian Bucher (who describes himself as “just an engineer, not a statistician”) writes: I’ve read your paper with John Carlin, Beyond Power Calculations. Would you happen to know of instances in the published or unpublished literature that implement this type of design analysis, especially using your retrodesign() function [here’s an updated version from Andy Timm], […]

What can be learned from this study?

James Coyne writes: A recent article co-authored by a leading mindfulness researcher claims to address the problems that plague meditation research, namely, underpowered studies; lack of or meaningful control groups; and an exclusive reliance on subjective self-report measures, rather than measures of the biological substrate that could establish possible mechanisms. The article claims adequate sample […]

Here are some examples of real-world statistical analyses that don’t use p-values and significance testing.

Joe Nadeau writes: I’ve followed the issues about p-values, signif. testing et al. both on blogs and in the literature. I appreciate the points raised, and the pointers to alternative approaches. All very interesting, provocative. My question is whether you and your colleagues can point to real world examples of these alternative approaches. It’s somewhat […]

Conditional probability and police shootings

A political scientist writes: You might have already seen this, but in case not: PNAS published a paper [Officer characteristics and racial disparities in fatal officer-involved shootings, by David Johnson, Trevor Tress, Nicole Burkel, Carley Taylor, and Joseph Cesario] recently finding no evidence of racial bias in police shootings: Jonathan Mummolo and Dean Knox noted […]

You are invited to join Replication Markets

Anna Dreber writes: Replication Markets (RM) invites you to help us predict outcomes of 3,000 social and behavioral science experiments over the next year. We actively seek scholars with different voices and perspectives to create a wise and diverse crowd, and hope you will join us. We invite you – your students, and any other […]