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Archive of posts filed under the Miscellaneous Statistics category.

Go to PredictWise for forecast probabilities of events in the news

I like it. Clear, transparent, no mumbo jumbo about their secret sauce. But . . . what’s with the hyper-precision: C’mon. “27.4%”? Who are you kidding?? (See here for explication of this point.)

Perhaps the most contextless email I’ve ever received

Date: February 3, 2015 at 12:55:59 PM EST Subject: Sample Stats Question From: ** Hello, I hope all is well and trust that you are having a great day so far. I hate to bother you but I have a stats question that I need help with: How can you tell which group has the […]

A message I just sent to my class

I wanted to add some context to what we talked about in class today. Part of the message I was sending was that there are some stupid things that get published and you should be careful about that: don’t necessarily believe something, just cos it’s statistically significant and published in a top journal. And, sure, […]

“For better or for worse, academics are fascinated by academic rankings . . .”

I was asked to comment on a forthcoming article, “Statistical Modeling of Citation Exchange Among Statistics Journals,” by Christiano Varin, Manuela Cattelan and David Firth. Here’s what I wrote: For better or for worse, academics are fascinated by academic rankings, perhaps because most of us reached our present positions through a series of tournaments, starting […]

But when you call me Bayesian, I know I’m not the only one

Textbooks on statistics emphasize care and precision, via concepts such as reliability and validity in measurement, random sampling and treatment assignment in data collection, and causal identification and bias in estimation. But how do researchers decide what to believe and what to trust when choosing which statistical methods to use? How do they decide the […]

How is ethics like logistic regression?

Ethics decisions, like statistical inferences, are informative only if they’re not too easy or too hard. For the full story, read the whole thing.

Regression: What’s it all about? [Bayesian and otherwise]

Regression: What’s it all about? Regression plays three different roles in applied statistics: 1. A specification of the conditional expectation of y given x; 2. A generative model of the world; 3. A method for adjusting data to generalize from sample to population, or to perform causal inferences. We could also include prediction, but I […]

Paul Meehl continues to be the boss

Lee Sechrest writes: Here is a remarkable paper, not well known, by Paul Meehl. My research group is about to undertake a fresh discussion of it, which we do about every five or ten years. The paper is now more than a quarter of a century old but it is, I think, dramatically pertinent to […]

“In general I think these literatures have too much focus on data analysis and not enough on data collection.”

Mike Zyphur pointed me to an article appearing in Psychological Bulletin with a meta-analysis of ovulatory cycle effects: Title: Do Women’s Mate Preferences Change Across the Ovulatory Cycle? A Meta-Analytic Review Authors: Gildersleeve, K; Haselton, MG; Fales, MR Source: PSYCHOLOGICAL BULLETIN , 140 (5):1205-1259; SEP 2014 Abstract: Scientific interest in whether women experience changes across […]

Stock-and-flow and other concepts that are important in statistical modeling but typically don’t get taught to statisticians

Bill Harris writes: You’ve written about causality somewhat often, and you, along with perhaps everyone who has done anything with statistics, have written that “correlation is not causation.” When you say that correlation is not causation, you seem to be pointing out cases where correlation exists but causality does not. While that’s important, there’s another […]