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Archive of posts filed under the Teaching category.

Understanding Simpson’s paradox using a graph

Joshua Vogelstein pointed me to this post by Michael Nielsen on how to teach Simpson’s paradox. I don’t know if Nielsen (and others) are aware that people have developed some snappy graphical methods for displaying Simpson’s paradox (and, more generally, aggregation issues). We do some this in our Red State Blue State book, but before […]

Just gave a talk

I just gave a talk in Milan. Actually I was sitting at my desk, it was a g+ hangout which was a bit more convenient for me. The audience was a bunch of astronomers so I figured they could handle a satellite link. . . . Anyway, the talk didn’t go so well. Two reasons: […]

A statistical graphics course and statistical graphics advice

Dean Eckles writes: Some of my coworkers at Facebook and I have worked with Udacity to create an online course on exploratory data analysis, including using data visualizations in R as part of EDA. The course has now launched at https://www.udacity.com/course/ud651 so anyone can take it for free. And Kaiser Fung has reviewed it. So definitely feel free […]

Teaching Bayesian applied statistics to graduate students in political science, sociology, public health, education, economics, . . .

One of the most satisfying experiences for an academic is when someone asks a question that you’ve already answered. This happened in the comments today. Daniel Gotthardt wrote: So for applied stat courses like for sociologists, political scientists, psychologists and maybe also for economics, what do we actually want to accomplish with our intro courses? […]

The candy weighing demonstration, or, the unwisdom of crowds

From 2008: The candy weighing demonstration, or, the unwisdom of crowds My favorite statistics demonstration is the one with the bag of candies. I’ve elaborated upon it since including it in the Teaching Statistics book and I thought these tips might be useful to some of you. Preparation Buy 100 candies of different sizes and […]

Selection bias in the reporting of shaky research

I’ll reorder this week’s posts a bit in order to continue on a topic that came up yesterday. A couple days ago a reporter wrote to me asking what I thought of this paper on Money, Status, and the Ovulatory Cycle. I responded: Given the quality of the earlier paper by these researchers, I’m not […]

What is the appropriate time scale for blogging—the day or the week?

I post (approximately) once a day and don’t plan to change that. I have enough material to post more often—for example, I could intersperse existing blog posts with summaries of my published papers or of other work that I like; and, beyond this, we currently have a one-to-two-month backlog of posts—but I’m afraid that if […]

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 […]

Outrage of the week

Mark Palko passes this one along from high school principal Carol Burris: My music teacher, Doreen, brought me her second-grade daughter’s math homework. She was already fuming over Education Secretary Arne Duncan’s remark about why “white suburban moms” oppose the Common Core, and the homework added fuel to the fire. The problem that disturbed her […]

Parables vs. stories

God is in every leaf of every tree, but he is not in every leaf of every parable. Let me explain with a story. A few months ago I read the new book, Doing Data Science, by Rachel Schutt and Cathy O’Neal, and I came across the following motivation for comprehensive integration of data sources, […]