Syllabus

Logistics

This course is aimed at Ph.D. Students at IT University of Copenhagen
To be offered: Second half of Spring term 2017
Credit: 2 ECTS
Time and Location: Tuesdays, 13:00-14:00. First meeting: April 4.
Enrollment: 15–20 students
Instructors: Thore Husfeldt

The course will be offered as a 2-ECTS seminar, reading-group style, aimed at Ph.D. Students with different epistemological backgrounds.


Learning Objectives

Our learning objectives are straightforward. After taking the course, you should be able to:

  • Remain vigilant for bullshit contaminating your information diet.
  • Recognize said bullshit whenever and wherever you encounter it.
  • Figure out for yourself precisely why a particular bit of bullshit is bullshit.
  • Provide a statistician or fellow scientist with a technical explanation of why a claim is bullshit.

We will be astonished if these skills do not turn out to be among the most useful and most broadly applicable of those that you acquire during the course of your college education.

A second, important objective of this course is as a playground/warm-up for the the design of an undergraduate course on ITU’s upcoming Data Science eduction. Which part of this set of ideas works in a higher education context? Which skills are useful? What is teachable, and how can that be tested?


Preliminary schedule and readings

Each of the lectures will explore one specific facet of bullshit. For each week, a set of required readings are assigned. For some weeks, supplementary readings are also provided for those who wish to delve deeper.

At least in the beginning, we follow the UW course religiously.

Lectures

  1. Introduction to bullshit
  2. Spotting bullshit
  3. The natural ecology of bullshit
  4. Causality
  5. Statistical traps 
  6. Visualization
  7. Big data
  8. Publication bias 
  9. Predatory publishing and scientific misconduct
  10. The ethics of calling bullshit.
  11. Fake news
  12. Refuting bullshit
  13. More to be announced

Lecture 1. Introduction to bullshit. What is bullshit? Concepts and categories of bullshit. The art, science, and moral imperative of calling bullshit. Brandolini’s Bullshit Asymmetry Principle.

Supplementary readings

  • G. A. Cohen (2002) Deeper into Bullshit. Buss and Overton, eds., Contours of Agency: Themes from the Philosophy of Harry Frankfurt Cambridge, Massachusetts: MIT Press.
  • Philip Eubanks and John D. Schaeffer (2008) A kind word for bullshit: The problem of academic writing. College Composition and Communication 59(3): 372-388

Lecture 2. Spotting bullshit. Truth, like liberty, requires eternal vigilance. How do you spot bullshit in the wild? Effect sizes, dimensions, Fermi estimation, and checks on plausibility. Claims and the interests of those who make them. Forensic data analysis: GRIM test, NewcombBenford law.


 Lecture 3. The natural ecology of bullshit.  Where do we find bullshit? Why news media provide bullshit. TED talks and the marketplace for upscale bullshit. Why social media provide ideal conditions for the growth and spread of bullshit.


Lecture 4. Causality One common source of bullshit data analysis arises when people ignore, deliberately or otherwise, the fact that correlation is not causation. The consequences can be hilarious, but this confusion can also be used to mislead. Regression to the mean pitched as treatment effect. Selection masked as transformation.

Supplementary reading

  • Karl Pearson (1897) On a Form of Spurious Correlation which may arise when Indices are used in the Measurement of Organs. Proceedings of the Royal Society of London 60: 489–498. For context see also Aldrich (1995).

 Lecture 5. Statistical traps and trickery. Base-rate fallacy / prosecutor’s fallacy. Simpson’s paradox. Data censoring. Will Rogers effect, lead-time bias, and length time bias. Means versus medians. Importance of higher moments.


Lecture 6. Data visualization. Data graphics can be powerful tools for understanding information, but they can also be powerful tools for misleading audiences. We explore the many ways that data graphics can steer viewers toward misleading conclusions.

  • Edward Tufte (1983) The Visual Display of Quantitative InformationChartjunk: vibrations, grids, and ducks. (Chapter 5)
  • Tools and tricks: Misleading axes

Lecture 7. Big data. When does any old algorithm work given enough data, and when is it garbage in, garbage out? Use and abuse of machine learning. Misleading metrics. Goodhart’s law.

Supplementary reading

  • Cathy O’Neil (2016) Weapons of Math Destruction Crown Press.

Lecture 8. Publication bias. Even a community of competent scientists all acting in good faith can generate a misleading scholarly record when — as is the case in the current publishing environment — journals prefer to publish positive results over negative ones. In a provocative and hugely influential 2005 paper, epidemiologist John Ioannides went so far as to argue that this publication biashas created a situation in which most published scientific results are probably false. As a result, it’s not clear that one can safely rely on the results of some random study reported in the scientific literature, let alone on Buzzfeed.

Supplementary Reading


Lecture 9. Predatory publishing and scientific misconduct. Predatory publishing. Beall’s list and his anti-Open Access agenda. Publishing economics. Pathologies of publish-or-perish culture.

    • Fake academe looking much like the real thing.New York Times

Dec. 29, 2016.


 Lecture 10. The ethics of calling bullshit. Where is the line between deserved criticism and targeted harassment? 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? Pubpeer. Principles for the ethical calling of bullshit. Differences between being a hard-minded skeptic and being a domineering jerk.


Lecture 11. Fake news.. Fifteen years ago, nascent social media platforms offered the promise of a more democratic press through decentralized broadcasting and a decoupling of publishing from advertising revenue. Instead, we get sectarian echo chambers and, lately, a serious assault on the very notion of fact. Not only did fake news play a substantive role in the November 2016 US elections, but recently a fake news story actually provoked nuclear threats issued by twitter.

New York Times


Lecture 12. Refuting bullshit. Refuting bullshit requires different approaches for different audiences. What works for a quantitatively-skilled professional scientist won’t always convince your casually racist uncle on facebook, and vice versa.

Additional readings @ ITU

Here is some additional material that Thore finds useful and enlightening:

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