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Archive of posts filed under the Miscellaneous Statistics category.

Preregistration: what’s in it for you?

Chris Chambers pointed me to a blog by someone called Neuroskeptic who suggested that I preregister my political science studies: So when Andrew Gelman (let’s say) is going to start using a new approach, he goes on Twitter, or on his blog, and posts a bare-bones summary of what he’s going to do. Then he […]

“We are moving from an era of private data and public analyses to one of public data and private analyses. Just as we have learned to be cautious about data that are missing, we may have to be cautious about missing analyses also.”

Stephen Senn writes: For many years now I [Senn] have been making the point that obtaining a license to market a drug should carry with it the obligation to share the results with interested parties. . . . Amongst those misunderstanding the issues, are many who work in the pharmaceutical industry. A common assumption is […]

God/leaf/tree

Govind Manian writes: I wanted to pass along a fragment from Lichtenberg’s Waste Books — which I am finding to be great stone soup — that reminded me of God is in Every Leaf: To the wise man nothing is great and nothing small…I believe he could write treatises on keyholes that sounded as weighty […]

Econometrics, political science, epidemiology, etc.: Don’t model the probability of a discrete outcome, model the underlying continuous variable

This is an echo of yesterday’s post, Basketball Stats: Don’t model the probability of win, model the expected score differential. As with basketball, so with baseball: as the great Bill James wrote, if you want to predict a pitcher’s win-loss record, it’s better to use last year’s ERA than last year’s W-L. As with basketball […]

“Edlin’s rule” for routinely scaling down published estimates

A few months ago I reacted (see further discussion in comments here) to a recent study on early childhood intervention, in which researchers Paul Gertler, James Heckman, Rodrigo Pinto, Arianna Zanolini, Christel Vermeerch, Susan Walker, Susan M. Chang, and Sally Grantham-McGregor estimated that a particular intervention on young children had raised incomes on young adults […]

Quickies

I received a few emails today on bloggable topics. Rather than expanding each response into a full post, I thought I’d just handle them all quickly.

The replication and criticism movement is not about suppressing speculative research; rather, it’s all about enabling science’s fabled self-correcting nature

Jeff Leek points to a post by Alex Holcombe, who disputes the idea that science is self-correcting. Holcombe writes [scroll down to get to his part]: The pace of scientific production has quickened, and self-correction has suffered. Findings that might correct old results are considered less interesting than results from more original research questions. Potential […]

Stopping rules and Bayesian analysis

I happened to receive two questions about stopping rules on the same day. First, from Tom Cunningham: I’ve been arguing with my colleagues about whether the stopping rule is relevant (a presenter disclosed that he went out to collect more data because the first experiment didn’t get significant results) — and I believe you have […]

How to think about “identifiability” in Bayesian inference?

We had some questions on the Stan list regarding identification. The topic arose because people were fitting models with improper posterior distributions, the kind of model where there’s a ridge in the likelihood and the parameters are not otherwise constrained. I tried to help by writing something on Bayesian identifiability for the Stan list. Then […]

My talks in Bristol this Wed and London this Thurs

1. Causality and statistical learning (Wed 12 Feb 2014, 16:00, at University of Bristol): Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are […]