You’ll sometimes see discussions of the differences between two different approaches to classical statistical null hypothesis testing. In the Fisher approach, you set up a null hypothesis and then you compute the p-value, which you use as a measure of evidence against the hypothesis. In the Neyman-Pearson approach, you define the p-value as a function […]
Archive of posts filed under the Miscellaneous Statistics category.
We have really everything in common with machine learning nowadays, except, of course, language.
I had an interesting exchange with Bob regarding the differences between statistics and machine learning. If it were just differences in jargon, it would be no big deal—you could just translate back and forth—but it’s trickier than that, because the two subfields also have different priorities and concepts. It started with this abstract by Satyen […]
How to convince yourself that multilevel modeling (or, more generally, any advanced statistical method) has benefits?
Someone who would like to remain anonymous writes: I have read your blog posts discussing the benefits of Bayesian inference and partial pooling from a multilevel modeling approach. Recently, I’ve begun thinking about designing a simulation to prove to myself that these methods provide superior performance. Here’s what I have thus far: Perhaps I could […]
Statisticians don’t use statistical evidence to decide what statistical methods to use. Also, The Way of the Physicist.
David Bailey, a physicist at the University of Toronto, writes: I thought you’d be pleased to hear that a student in our Advanced Physics Lab spontaneously used Stan to analyze data with significant uncertainties in both x and y. We’d normally expect students to use python and orthogonal distance regression, and STAN is never mentioned […]
“Smell the Data”
Mike Maltz writes the following on ethnography and statistics: I got interested in ethnographic studies because of a concern for people analyzing data without an understanding of its origins and the way it was collected. An ethnographer collects stories, and too many statisticians disparage them, calling them “anecdotes” instead of real data. But stories are […]
A new idea for a science core course based entirely on computer simulation
Lizzie writes: Thinking on what to do for my now-online stats course next term, I stumbled on your re-post from 2016. And wanted to ask if you or anyone did this (that you know of)? Or if any of your in-development books do lots and lots and lots of simulation? The more I teach fake […]
This awesome Pubpeer thread is about 80 times better than the original paper
This came up already, but in the meantime this paper in the Journal of Surgical Research has been just raked over the coals, over and over and over again, in this delightful Pubpeer thread. 31 comments so far, all of them just slamming the original published paper and many with interesting insights of their own. […]
There is only one reality (and we cannot demand consistency from any other)
I bought The Shadow of the Torturer when it came out in paperback, I guess in response to a positive review. I found it kinda difficult to read, but I wanted to know what would happen next, so I bought volumes 2, 3, and 4 when they came out too. By the time I was […]
My reply: Three words. Fake. Data. Simulation.
Kash Ramli writes: I am planning on running an experiment to determine whether an adaptive treatment approach to behaviour change interventions could be effective at reducing the heterogenous treatment effects currently observed in the field. The context of the experiment is providing households with social norms based feedback of their consumption (i.e. comparing your consumption […]
New textbook, “Statistics for Health Data Science,” by Etzioni, Mandel, and Gulati
Ruth Etzioni, Micha Mandel, Roman Gulati wrote a new book that I really like. Here are the chapters: 1 Statistics and Health Data 1.1 Introduction 1.2 Statistics and Organic Statistics 1.3 Statistical Methods and Models 1.4 Health Care Data 1.5 Outline of the Text 1.6 Software and Data 2 Key Statistical Concepts 2.1 Samples and […]
You’re a data scientist at a local hospital and you’ve been asked to present to the physicians on communicating statistical information to patients. What should you say?
Someone who wishes to remain anonymous writes: I just read your post reflecting on crappy talks . . . I’m reaching out because I’m a data scientist at a local hospital in the US and I’ve been asked to present to our physicians about communicating statistical information to patients (e.g., how to interpret the results […]
Include all design information as predictors in your regression model, then postratify if necessary. No need to include survey weights: the information that goes into the weights will be used in any poststratification that is done.
David Kaplan writes: I have a question that comes up often when working with people who are analyzing large scale educational assessments such as NAEP or PISA. They want to do some kind of multilevel analysis of an achievement outcome such as mathematics ability predicted by individual and school level variables. The files contain the […]
Weakliem on air rage and himmicanes
Weakliem writes: I think I see where the [air rage] analysis went wrong. The dependent variable was whether or not an “air rage” incident happened on the flight. Two important influences on the chance of an incident are the number of passengers and how long the flight was (their data apparently don’t include the number […]
What is/are bad data?
This post is by Lizzie, I also took the picture of the cats. I was talking to a colleague about a recent paper, which has some issues, but I was a bit surprised by her response that one of the real issues was that it ‘just uses bad data.’ I snapped back reflexively, ‘it’s not […]
Typo of the day
“Poststratifiction”
What we did in 2020, and thanks to all our collaborators and many more
Published or to be published articles: [2021] Reflections on Lakatos’s “Proofs and Refutations.” {\em American Mathematical Monthly}. (Andrew Gelman) [2021] Holes in Bayesian statistics. {\em Journal of Physics G: Nuclear and Particle Physics}. (Andrew Gelman and Yuling Yao) [2021] Reflections on Breiman’s Two Cultures of Statistical Modeling. {\em Observational Studies}. (Andrew Gelman) [2021] Bayesian statistics […]
Retired computer science professor argues that decisions are being made by “algorithms that are mathematically incapable of bias.” What does this mean?
This came up in the comments, but not everyone reads the comments, so . . . Joseph recommended an op-ed entitled “We must stop militant liberals from politicizing artificial intelligence; ‘Debiasing’ algorithms actually means adding bias,” by retired computer science professor Pedro Domingos. The article begins: What do you do if decisions that used to […]
You can figure out the approximate length of our blog lag now.
Sekhar Ramakrishnan writes: I wanted to relate an episode of informal probabilistic reasoning that occurred this morning, which I thought you might find entertaining. Jan 6th is the Christian feast day of the Epiphany, which is known as Dreikönigstag (Three Kings’ Day), here in Zürich, Switzerland, where I live (I work at ETH). There is […]
17 state attorney generals, 100 congressmembers, and the Association for Psychological Science walk into a bar
I don’t have much to add to all that’s been said about this horrible story. The statistics errors involved are pretty bad—actually commonplace in published scientific articles, but mistakes that seem recondite and technical in a paper about ESP, say, or beauty and sex ratio, become much clearer when the topic is something familiar such […]
What are the most important statistical ideas of the past 50 years?
Aki and I wrote this article, doing our best to present a broad perspective. We argue that the most important statistical ideas of the past half century are: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis. These eight […]