You can call bullshit on bullshit, but you can also call bullshit on lies, treachery, trickery, or injustice. In this course we will teach you how to spot the former and effectively perform the latter.

Our case studies are not the most egregious examples of bullshit, nor the ones we most wish to debunk. Rather, they are chosen to serve a pedagogical purpose, drawing out particular pitfalls and highlighting appropriate strategies for responding. So read up, think carefully, and call bullshit yourself. I'm an instructor.

Is it, as one prominent scholar argued, “methodological terrorism” to call bullshit on a colleague's analysis? What if you use social media instead of a peer-reviewed journal to do so? How about calling bullshit on a whole field that you know almost nothing about?

Mar 29, 2017 · In this segment on unfair comparisons, Carl explains why St. Louis and Detroit are not quite as bad as clickbait "most dangerous cities" lists portray them to be, and looks at the silly arguments over attendance at Trump's inauguration. Also: how to call bullshit on algorithms and statistics without a PhD in machine learning or statistics.

In many of the course lectures we will discuss how you can spot bullshit, call bullshit, and avoid becoming the victim of bullshit. Here we present a set of instructional essays on various aspects of bullshit detection and refutation.

Spotting bullshit in the wild it isn't something you have to let others do for you. To illustrate this, we've provided a set of case studies based upon examples of bullshit in the wild.

Exercises Assignment 1: A bullshit inventory. There's a lot of bullshit out there — but how much exactly, and of what form? The purpose of our first assignment is for you explore this question by taking a "bullshit inventory" of all of the bullshit you encounter of the course of one week.

Misleading axes on graphs. The purpose of a publication-stage data visualization is to tell a story. Subtle choices on the part of the author about how to represent a dataset graphically can have a substantial influence on the story that a visualization tells.

Sep 19, 2017 · The central theme of our Calling Bullshit course is you don't always need to understand the technical details of a statistical analysis or computer algorithm to call bullshit on its use. As illustrated in the diagram below, there are typically several steps that people go through when they construct quantitative arguments.

When the authors break the criminal and non-criminal down into what they call “subtypes”, the smiling/non-smiling distinction becomes even more readily apparent. Below is their figure illustrating what they call four criminal subtypes at top and three non-criminal subtypes at bottom.