Course Lectures, Spring 2017

Calling Bullshit, Spring 2017

We will record and post all ten lectures from our Spring 2017 course. Each will be presented here as it becomes available. We have divided up every lecture into a set of a shorter segments; these segments should more or less stand alone on their own merits. The full playlist of all course videos is available on the UW Information School's YouTube channel.

Lecture 1: An Introduction to Bullshit

1.1 Introduction to Bullshit.
Bullshit is everywhere, and we've had enough. We want to teach people to detect and defuse bullshit where ever it may arise.

1.2 Calling Bullshit on Ourselves.
Jevin uses data graphics to boast about explosive growth at our website — and Carl calls bullshit. Old-school bullshit versus new-school bullshit.

1.3 Brandolini's Bullshit Asymmetry Principle.
Lecture 1.3 "The amount of effort necessary to refute bullshit is an order of magnitude bigger than to produce it."

1.4 Classroom Discussion.
Students discuss: What is bullshit anyway?

1.5 The Philosophy of Bullshit.
How do we define bullshit? Does intention matter? Calling bullshit as a speech act.

Lecture 2: Spotting Bullshit

2.1 Spotting Bullshit.
Jevin discusses some ways to spot bullshit and challenges students to tell whether four nuggets of wisdom from the internet are true or bullshit.

2.2 Sounds Too Good to be True.
If a claim seems too good — or too bad — to be true, it probably is. An example involving recommendation letters, and the perils of confirmation bias.

2.3 Entertain Multiple Hypotheses.
The importance of generating and considering multiple alternative hypotheses. As an example, we consider why men cite themselves more than women do.

2.4 Fermi Estimation.
Using Fermi estimation to check the plausibility of claims, with an example of food stamp fraud. This example is treated in further detail in one of our case studies.

2.5 Unfair Comparisons.
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.

2.6 Assignment: Bullshit Inventory.
In our first assignment, we ask students to take a week-long bullshit inventory of the bullshit they encounter, create, and debunk.