Course Lectures, Spring 2017

Calling Bullshit, Spring 2017

In Spring 2017, we taught the course for the first time as a series of ten hour-long lectures. These lectures were recorded using multiple cameras and edited to form a video series. We have divided up every lecture into a set of a shorter segments; each segment should more or less stand alone on its 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

March 29, 2017

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

April 5, 2017

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.

Lecture 3: Correlation and Causation

April 12, 2017

3.1 Correlation and Causation
Correlations are often used to make claims about causation. Be careful about the direction in which causality goes. For example: do food stamps cause poverty?

3.2 What are Correlations?
Jevin providers an informal introduction to linear correlations.

3.3 Spurious Correlations?
We look at Tyler Vigen’s silly examples of quantities appear to be correlated over time, and note that scientific studies may accidentally pick up on similarly meaningless relationships.

3.4 Correlation Exercise
When is correlation all you need, and causation is beside the point? Can you figure out which way causality goes for each of several correlations?

3.5 Common Causes
We explain how common causes can generate correlations between otherwise unrelated variables, and look at the correlational evidence that storks bring babies. We look at the need to think about multiple contributing causes. The fallacy of post hoc propter ergo hoc: the mistaken belief that if two events happen sequentially, the first must have caused the second.

3.6 Manipulative Experiments
We look at how manipulative experiments can be used to work out the direction of causation in correlated variables, and sum up the questions one should ask when presented with a correlation.

Lecture 4: Statistical Traps and Trickery

April 19, 2017

4.1 Right Censoring
We look at a graph of age at death for musicians in different genres, and use this to illustrate the problem of right-censored data. We consider this issue in further detail in one of our case studies.

4.2 Means and Medians
Simple as it may sound, the difference between mean and median values offers fertile ground for cooking up misleading statistics.

4.3 p-Values and the Prosecutor’s Fallacy
Carl presents what he thinks may be one of the most important segments in the whole course: a discussion of the prosecutor’s fallacy. This logical fallacy is not limited to the courtroom: it underlies a very common misinterpretation of the p values associated with scientific experiments.

4.4 The Will Rogers Effect
Will Rogers purportedly quipped that when the Okies left Oklahoma for California, they raised the average intelligence in both states. The same phenomenon can arise in epidemiology and a host of other areas.

4.5 Jevin's Turn
Jevin goes looking for bullshit and finds it — in Carl’s textbook. Jevin calls bullshit on Carl’s use of track and field records by age to illustrate senescence, and Carl tries to explain himself. This example is described further in another of our case studies.

Lecture 5: Big Data

April 26, 2017

5.1 Big Data Introduction
We briefly introduce big data and provide a few the cautionary tales surrounding this recent phenomenon. Beware of those ponies…

5.2 Garbage In, Garbage Out
You don’t need a PhD in statistics or machine learning to call bullshit on big data. Simply by focusing on the input data and the results is often sufficient to refute a claim.

5.3 Big Data Hubris
We discuss the Google Flu Trends project and how it moved from being a poster child for big data to a providing an important precautionary tale.

5.4 Overfitting
We examine overfitting, the Achilles heel of machine learning. We illustrate overfitting visually, and consider and what to look out for.

5.5 Criminal Machine Learning
A recent paper claims that machine learning can determine whether or not you are a criminal from a photograph of your face. That's bullshit. This example is described further in one of our case studies.

5.6 Algorithmic Ethics
We discuss gender and racial biases inherent to many of the machine learning algorithms and recommender systems prevalent in today’s technology, and encourage others to call bullshit on machine injustice.

Lecture 6: Data Visualization

May 3, 2017

6.1 Dataviz in the Popular Media
Until recently, the popular media made minimal use of sophisticated data visualization. People have not necessarily had time to hone their bullshit detectors for application to data graphics.

6.2 Misleading Axes
One of the most common abuses of data visualization involves the inappropriate ranges on the dependent variable (y) axis. Carl looks at a series of example, and explain why bar charts should include zero whereas line graphs need not — and often should not — do so. This example is treated in further detail in one of our articles.

6.3 Manipulating Bin Sizes
By binning data in different ways, bar charts can be made to tell very different stories. Here we consider an example from the Wall Street Journal.

6.4 Dataviz Ducks
Edward Tufte uses the term “ducks” to refer to data graphics that put style ahead of substance. We explain why, and explore a number of examples.

6.5 Glass Slippers
We propose the term “glass slipper” to describe to data visualizations in which the designer has taken a beautiful data design intended for very specific situations, and tried to shoehorn entirely inappropriate types of data into it. Carl considers examples including a periodic table of data science, a subway map of corporate acquisitions, a phylogenetic tree of internet marketing, and numerous Venn diagrams.

6.6 The Principle of Proportional Ink
Our principle of proportional ink states that when a shaded region is used to represent a numerical value, the area of that shaded region should be directly proportional to the corresponding value. We look at graphs that violate this principle and discuss how such violations can be misleading. This example is treated in further detail in one of our articles.

Lecture 7: Publication bias

May 10, 2017

7.1 Duck hunting
For last week’s homework assignment, students searched for examples of “duck” and “glass slipper” data visualizations. Carl and Jevin look at a few of the best finds.

7.2 Science is amazing, but…
Science is probably the greatest human invention of all time, but that doesn’t mean it doesn’t come with its share of bullshit..

7.3 Reproducibility
Jevin discusses how spreadsheet errors reversed the conclusions of a high-profile paper that was used to justify austerity measures.

7.4 A Replication Crisis
Scientists have difficulty reproducing a surprisingly large fraction of the published literature. What is going on?

7.5 Publication Bias
Journals prefer to publish positive results and scientists prefer to submit successful experiments. This can be misleading given that we typically can look only at the published literature.

7.6 Science is not Bullshit
The subject matter of today’s lecture notwithstanding, science generally works pretty darn well. We can build airplanes and iPhones and save lives with antibiotics and vaccines, after all. Carl looks at five reasons why this is true.

Lecture 8: Scholarly publishing and predatory publishers

May 17, 2017

8.1 What Motivates Scientists?
Scientists are not purely seeking knowledge; like anyone else, they are also pursuing fame and fortune. If we can understand the incentives that scientists face, we can better understand why they do what they do.

8.2 An Overview of Scholarly Publishing
Jevin talks about how scholarly publishing has become a big business, and describes the rise of the open access publishing model.

8.3 Predatory Publishing
We introduce the world of so-called predatory or otherwise questionable scientific publishers, and consider the reasons that authors publish in them anyway.

8.4 Reputable or Questionable?
We challenge our audience to distinguish papers published in reputable journals from papers published in journals from so-called predatory publishers.

8.5 Journal Spam
Carl explores the scourge of journal spam and some of the humorous ways that academics have fought back.

Lecture 9: Fake News

May 24, 2017

9.1 The Spreading of Fake News
We investigate some of the top fake news stories from the last year, and discuss how our digital environments facilitate the spreading of this information.

9.2 Fake News Definitions and Examples
We present examples and definitions of fake news. We explore how this white noise is being shared millions of times from both sides of the political aisle, making people lots of money, and fooling our search engines, which can make it difficult for democracy to run effectively.

9.3 The Ecology of Fake News
Fake news is not a new thing. It has been around for a long time — but social media, bots, and highly partisan environments are spreading fake news ever more readily.

9.4 Sharing as Social Signaling
We are all publishers. Often we share information as a signal of our group membership. Click-driven publishing models are facilitating the spread of this fake news.

9.5 Stamping out Fake News
Resources exist for checking and stopping fake news. Remember to question a source of information and do you best not to spread articles you haven’t checked. Consider supporting high quality journalism with a subscription.

Lecture 10: Refuting Bullshit

May 24, 2017

10.1 Four Rules of Calling Bullshit
We consider four rules for calling bullshit: Be correct; Be charitable; Be clear; Admit fault.

10.2 Reductio ad Absurdum
The method of reductio ad absurdum, in which an argument’s methods are shown to lead to ridiculous conclusions, is extremely powerful for refuting bullshit claims. We examine statistical projections of gold medal 100 meter times and the cognitive-emotional responses of a dead salmon.

10.3 Debunking Myths
Jevin introduces Cook and Lewandowsky’s Debunking Handbook, and suggests a number of rules for how to successfully change opinions rather than reinforcing erroneous beliefs.

10.4 Deploying Null Models and Tracing the Origin of Falsehoods
First, models can sometimes be used to show that the evidence someone presents does not require the process for which they use it as evidence. Second, people are more readily convinced of a falsehood when they are shown where in the communication chain the falsehood arose.

10.5 Counterexamples and analogies
A single counterexample can demolish an elaborate argument, and a well-chosen analogy can draw out the fallacious reasoning underlying an argument.

10.6 Walk away; Conclusions
Carl stresses the important of being willing to walk away, and offers a few parting thoughts.