How to Detect Bullshit

Carl Bergstrom and Jevin West, two professors from the University of Washington, have recently developed a course which teaches college students how they can recognize and challenge everyday bullshit. Fact-checking seems particularly relevant given our current political climate, and Bergstrom and West have focused their syllabus on the misuse of statistics and graphs which directly contradict empirical facts. When we hear outright lies from friends, family, elected representatives, advertisers, corporations, teachers, etc., we can normally uncover the truth with careful research. Bergstrom and West ask how much this situation changes when data itself becomes one of the basic means of warping reality. If scientists, politicians, news outlets, and spokespersons misrepresent and bury facts with rhetoric, statistics, and graphics, then we must consider when, how, and why others might manipulate us. Bergstrom and West themselves claim that bullshit does not end with news and politics: it enters academic journals, TED lectures, published books, and (perhaps most problematically) classrooms nationwide.

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The course website includes specific case-studies of statistical bullshit, academic essays about the spread and detection of bullshit, and clear statements of purpose from its two architects. I highly recommend the site for instructors concerned with how well their students analyze evidence, and the cases from the website can spark useful discussions about why even 99% caffeine-free products still contain high doses of caffeine and why over $70 million of fraud should not end the national food stamp program. Students can then locate and discuss their own examples of popular and academic bullshit, and the authors of the course have requested more case-studies and articles for future versions of their syllabus. You can find the website here: Calling Bullshit Course Website.

This course and its materials raise serious questions about the relationship between facts, statistics, and reality. We often assume statistics and graphs are self-explanatory, yet the flood of numbers we collect and cite share the fundamental drawbacks of the words they complement and sometimes replace. Data changes with the addition and removal of analytical context, and we cannot trust graphics, tables, and statistics simply because they carry the authority of mathematics and the sciences. The problem of bullshit lies at the center of technical communication: when does the combination of carefully-selected methodologies, quantitative and qualitative results, and technical vocabulary help us secure accuracy and accountability, and when does it simply overwhelm its audiences with the appearance of credibility and objectivity? Why do we question words more than we evaluate numbers?

Before ending this post, I would like to thank everyone who has visited the website since my last article two months ago. I will hopefully resume my original schedule of one-two posts each week for the immediate future. I welcome your comments and any resources that seem appropriate for the site, and thank you again for your support.

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