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 […]

**Teaching**category.

## 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 […]

## What readings should be included in a seminar on the philosophy of statistics, the replication crisis, causation, etc.?

André Ariew writes: I’m a philosopher of science at the University of Missouri. I’m interested in leading a seminar on a variety of current topics with philosophical value, including problems with significance tests, the replication crisis, causation, correlation, randomized trials, etc. I’m hoping that you can point me in a good direction for accessible readings […]

## It’s hard to know what to say about an observational comparison that doesn’t control for key differences between treatment and control groups, chili pepper edition

Jonathan Falk points to this article and writes: Thoughts? I would have liked to have seen the data matched on age, rather than simply using age in a Cox regression, since I suspect that’s what really going on here. The non-chili eaters were much older, and I suspect that the failure to interact age, or […]

## Seemingly intuitive and low math intros to Bayes never seem to deliver as hoped: Why?

This post was prompted by recent nicely done videos by Rasmus Baath that provide an intuitive and low math introduction to Bayesian material. Now, I do not know that these have delivered less than he hoped for. Nor I have asked him. However, given similar material I and others have tried out in the past that […]

## Giving feedback indirectly by invoking a hypothetical reviewer

Ethan Bolker points us to this discussion on “How can I avoid being “the negative one” when giving feedback on statistics?”, which begins: Results get sent around a group of biological collaborators for feedback. Comments come back from the senior members of the group about the implications of the results, possible extensions, etc. I look […]

## Animating a spinner using ggplot2 and ImageMagick

It’s Sunday, and I [Bob] am just sitting on the couch peacefully ggplotting to illustrate basic sample spaces using spinners (a trick I’m borrowing from Jim Albert’s book Curve Ball). There’s an underlying continuous outcome (i.e., where the spinner lands) and a quantization into a number of regions to produce a discrete outcome (e.g., “success” […]

## No, I’m not blocking you or deleting your comments!

Someone wrote in: I am worried you may have blocked me from commenting on your blog (because a couple of comments I made aren’t there). . . . Or maybe I failed to post correctly or maybe you just didn’t think my comments were interesting enough. . . . This comes up from time to […]

## Bad Numbers: Media-savvy Ivy League prof publishes textbook with a corrupted dataset

[cat picture] I might not have noticed this one, except that it happened to involve Congressional elections, and this is an area I know something about. The story goes like this. I’m working to finish up Regression and Other Stories, going through the examples. There’s one where we fit a model to predict the 1988 […]

## Kaiser Fung’s data analysis bootcamp

Kaiser Fung announces a new educational venture he’s created, a bootcamp (12-week full-time in-person program with a curriculum) of short courses with a goal of getting people their first job in an analytics role for a business unit (not engineering or software development, so he is not competing directly with MS Data Science or data […]