[image of a cat with a fork] Kevin Lewis points us to this paper which begins: We use a regression discontinuity design to estimate the causal effect of election to political office on natural lifespan. In contrast to previous findings of shortened lifespan among US presidents and other heads of state, we find that US […]

**Teaching**category.

## Stupid-ass statisticians don’t know what a goddam confidence interval is

From page 20 in a well-known applied statistics textbook: The hypothesis of whether a parameter is positive is directly assessed via its confidence interval. If both ends of the 95% confidence interval exceed zero, then we are at least 95% sure (under the assumptions of the model) that the parameter is positive. Huh? Who says […]

## Interactive visualizations of sampling and GP regression

You really don’t want to miss Chi Feng‘s absolutely wonderful interactive demos. (1) Markov chain Monte Carlo sampling I believe this is exactly what Andrew was asking for a few Stan meetings ago: Chi Feng’s Interactive MCMC Sampling Visualizer This tool lets you explore a range of sampling algorithms including random-walk Metropolis, Hamiltonian Monte Carlo, […]

## Stan is a probabilistic programming language

See here: Stan: A Probabilistic Programming Language. Journal of Statistical Software. (Bob Carpenter, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, Allen Riddell) And here: Stan is Turing Complete. So what? (Bob Carpenter) And, the pre-stan version: Fully Bayesian computing. (Jouni Kerman and Andrew Gelman) Apparently […]

## Tips when conveying your research to policymakers and the news media

Following up on a conversation regarding publicizing scientific research, Jim Savage wrote: Here’s a report that we produced a few years ago on prioritising potential policy levers to address the structural budget deficit in Australia. In the report we hid all the statistical analysis, aiming at an audience that would feel comfortable reading a broadsheet […]

## My talk tomorrow (Fri) 10am at Columbia

I’m speaking for the statistics undergraduates tomorrow (Fri 17 Nov) 10am in room 312 Mathematics Bldg. I’m not quite sure what I’ll talk about: maybe I’ll do again my talk on statistics and sports, maybe I’ll speak on the statistical crisis in science. Anyone can come; especially we’d like to attract undergraduates—not just statistics majors—to […]

## Looking for data on speed and traffic accidents—and other examples of data that can be fit by nonlinear models

[cat picture] For the chapter in Regression and Other Stories that includes nonlinear regression, I’d like a couple homework problems where the kids have to construct and fit models to real data. So I need some examples. We already have the success of golf putts as a function of distance from the hole, and I’d […]

## Advice for science writers!

I spoke today at a meeting of science journalists, in a session organized by Betsy Mason, also featuring Kristin Sainani, Christie Aschwanden, and Tom Siegfried. My talk was on statistical paradoxes of science and science journalism, and I mentioned the Ted Talk paradox, Who watches the watchmen, the Eureka bias, the “What does not kill […]

## My favorite definition of statistical significance

From my 2009 paper with Weakliem: Throughout, we use the term statistically significant in the conventional way, to mean that an estimate is at least two standard errors away from some “null hypothesis” or prespecified value that would indicate no effect present. An estimate is statistically insignificant if the observed value could reasonably be explained […]

## Why I think the top batting average will be higher than .311: Over-pooling of point predictions in Bayesian inference

In a post from 22 May 2017 entitled, “Who is Going to Win the Batting Crown?”, Jim Albert writes: At this point in the season, folks are interested in extreme stats and want to predict final season measures. On the morning of Saturday May 20, here are the leading batting averages: Justin Turner .379 Ryan […]

## Stan case studies

Following up on recent posts here and here, I thought I’d post a list of all the Stan case studies we have so far. 2017: Modeling Loss Curves in Insurance with RStan, by Mick Cooney Splines in Stan, by Milad Kharratzadeh Spatial Models in Stan: Intrinsic Auto-Regressive Models for Areal Data, by Mitzi Morris The […]

## Halifax, NS, Stan talk and course Thu 19 Oct

Halfiax, here we come. I (Bob, not Andrew) am going to be giving a talk on Stan and then Mitzi and I will be teaching a course on Stan after that. The public is invited, though space is limited for the course. Here are details if you happen to be in the Maritime provinces. TALK: […]

## Please contribute to this list of the top 10 do’s and don’ts for doing better science

Demis Glasford does research in social psychology and asks: I was wondering if you had ever considered publishing a top ten ‘do’s/don’ts’ for those of us that are committed to doing better science, but don’t necessarily have the time to devote to all of these issues [of statistics and research methods]. Obviously, there is a […]

## Tenure-Track or Tenured Prof. in Machine Learning in Aalto, Finland

This job advertisement for a position in Aalto, Finland, is by Aki We are looking for a professor to either further strengthen our strong research fields, with keywords including statistical machine learning, probabilistic modelling, Bayesian inference, kernel methods, computational statistics, or complementing them with deep learning. Collaboration with other fields is welcome, with local opportunities […]

## Alan Sokal’s comments on “Abandon Statistical Significance”

The physicist and science critic writes: I just came across your paper “Abandon statistical significance”. I basically agree with your point of view, but I think you could have done more to *distinguish* clearly between several different issues: 1) In most problems in the biomedical and social sciences, the possible hypotheses are parametrized by a […]

## For mortality rate junkies

Paul Ginsparg and I were discussing that mortality rate adjustment example. I pointed him to this old tutorial that laid out the age adjustment step by step, and he sent along this: For mortality rate junkies, here’s another example [by Steven Martin and Laudan Aron] of bundled stats lending to misinterpretation, in this case not […]

## Trial by combat, law school style

This story is hilarious. 78-year-old law professor was told he can no longer teach a certain required course; this jeopardizes his current arrangement where he is paid full time but only teaches one semester a year, so he’s suing his employer . . . Columbia Law School. The beautiful part of this story is how […]

## Self-study resources for Bayes and Stan?

Someone writes: I’m interested in learning more about data analysis techniques; I’ve bought books on Bayesian Statistics (including yours), on R programming, and on several other ‘related stuff’. Since I generally study this whenever I have some free time, I’m looking for sources that are meant for self study. Are there any sources that you […]

## Nice interface, poor content

Jim Windle writes: This might interest you if you haven’t seen it, and I don’t think you’ve blogged about it. I’ve only checked out a bit of the content but it seems a pretty good explanation of basic statistical concepts using some nice graphics. My reply: Nice interface, but their 3 topics of Statistical Inference […]

## Also holding back progress are those who make mistakes and then label correct arguments as “nonsensical.”

Here’s James Heckman in 2013: Also holding back progress are those who claim that Perry and ABC are experiments with samples too small to accurately predict widespread impact and return on investment. This is a nonsensical argument. Their relatively small sample sizes actually speak for — not against — the strength of their findings. Dramatic […]