Data Colada
Menu
  • Home
  • Table of Contents
  • Feedback Policy
  • About
Menu

Category: Teaching

[133] Heterofriendly: The Intuition for Why You Always Need Robust Standard Errors

Posted on March 2, 2026March 7, 2026 by Uri Simonsohn

When I taught my first PhD-level methods course, I invited students to submit questions about any topic in statistics or methodology. Six out of 10 students asked about the same topic: robust & clustered standard errors. It's clearly a topic they found both important and confusing. Psychologists basically never use robust standard errors. But they…

Read more

[56] TWARKing: Test-Weighting After Results are Known

Posted on January 3, 2017December 17, 2021 by Uri Simonsohn

On the last class of the semester I hold a “town-hall” meeting; an open discussion about how to improve the course (content, delivery, grading, etc). I follow-up with a required online poll to “vote” on proposed changes [1]. Grading in my class is old-school. Two tests, each 40%, homeworks 20% (graded mostly on a completion…

Read more

[26] What If Games Were Shorter?

Posted on August 22, 2014February 11, 2020 by Joe Simmons

The smaller your sample, the less likely your evidence is to reveal the truth. You might already know this, but most people don’t (.html), or at least they don’t appropriately apply it (.html). (See, for example, nearly every inference ever made by anyone). My experience trying to teach this concept suggests that it’s best understood…

Read more

[2] Using Personal Listening Habits to Identify Personal Music Preferences

Posted on September 26, 2013March 20, 2016 by Leif Nelson

Not everything at Data Colada is as serious as fraudulent data. This post is way less serious than that. This post is about music and teaching. As part of their final exam, my students analyze a data set. For a few years that data set has been a collection of my personal listening data from…

Read more

Get Colada email alerts.

Join 10.9K other subscribers

Social media

Recent Posts

  • [133] Heterofriendly: The Intuition for Why You Always Need Robust Standard Errors
  • [132] statuser: R in user-friendly mode
  • [131] Bending Over Backwards:
    The Quadratic Puts the U in AI
  • [130] ResearchBox: Even Easier to Use and More Transparently Permanent than Before
  • [129] P-curve works in practice, but would it work if you dropped a piano on it?

Get blogpost email alerts

Join 10.9K other subscribers

tweeter & facebook

We announce posts on Twitter
We announce posts on Bluesky
And link to them on our Facebook page

Posts on similar topics

Teaching
  • [133] Heterofriendly: The Intuition for Why You Always Need Robust Standard Errors
  • [56] TWARKing: Test-Weighting After Results are Known
  • [26] What If Games Were Shorter?
  • [2] Using Personal Listening Habits to Identify Personal Music Preferences

search

© 2021, Uri Simonsohn, Leif Nelson, and Joseph Simmons. For permission to reprint individual blog posts on DataColada please contact us via email..