Schedule

Most of the readings are available either via Sakai or hyperlinks. If you can’t find a reading, just ask!

Here’s your roadmap for the semester! Every day:

• read the assigned items
• post one question to the Sakai wiki
• complete your task if you are contextualizing, discussing, or summarizing
• pay attention to due dates for written assignments

Some general thoughts on discussions

Readings / Discussions / Due dates

Week 1 (perception)

8/31 Introductions, syllabus, logistics, webpage info

9/2 Context Discussion Summary: Professor Hardin

human perception

• Tversky, A. and Kahneman, D. (1974). “Judgment under uncertainty: heuristics and biases.” Science, vol 185, pgs 1124-1131. (https://library.claremont.edu/ – log in & search!)
  

• Tversky, A., & Kahneman, D. (1971). “Belief in the law of small numbers.” Psychological Bulletin, 76(2), 105–110. (https://library.claremont.edu/ – log in & search!)
  

• The Decision Lab, Why do we think a random event is more or less likely to occur if it happened several times in the past?“

• [optional] Utts, J. (2005/2015) “Psychological Influences on Personal Probabilities,” chp 17 & “When Intuition Differs from Relative Frequency,” chp 18. (on Sakai).

9/5 Due (Sunday by 11:59pm to Sakai): Micro-essay on chance

Week 2 (sports)

9/7 Context: Alan Discussion: Brendan Summary: JC

chance & variability in sports

• Berry, S. (2006) “Statistical fallacies in sports.” Chance, vol 19, pgs 50-56. (on Sakai)
  
• James, B., Albert, J., and Stern, H. (2005) “Answering questions about baseball using statistics.” Anthology of Statistics in Sports. Chp 15, pgs 111-117. (on Sakai)

9/9 Context: Skylar Discussion: Fiona Summary: Kyle

algorithms in sports

• Lewis, M. (2003) Moneyball, pgs 66 - 73 and 272 - 275. (on Sakai)
• Ross, T. (June 25, 2015) Welcome to smarter basketball.” The Atlantic.
  
• Rynston-Lobel, E. (May 21, 2021) The Kevin Kelley Way: The Unorthodox High School Coach Brings His Style to College Football Sports Illustrated.

9/12 Due (Sunday, 11:59pm to Sakai): Micro-essay on believing/doubting game (based on next Tuesday’s reading!)

Week 3 (fiction)

9/14 Context: Andrea Discussion: Nick Summary: Fiona

an interesting metaphor

• Borges, J.L. (1941) “The Babylon lottery.” Ficciones. (on Sakai)

9/16 [optional - but fun old movie with Jimmy Stewart] Magic Town (1947), directed by William A. Wellman, RKO Studios. (on Video47 via Sakai – choose any browser except Chrome)

9/16 Library Day - meet in th Digital Tool Shed in Honnold-Mudd library, see Sakai resources for map
Due (bring with you on paper): library notes (see Essay 1 link on Assignments tab)

9/17 Due (Friday, 11:59pm to Sakai): Micro-essay for Essay 1 (see Essay 1 link on Assignments tab)

Week 4 (sampling)

9/21 Context: Claire Discussion: Ty Summary: Nico

ideas of fate vs. chance

• Dostoevsky, F. (1866) The Gambler. (Access via Project Gutenberg)

9/23 Context: Aja Discussion: Jacqueline Summary: Ty

Sampling applet

random samples are key

• Bryson, M. (1976). “The Literary Digest poll: making of a statistical myth.” The American Statistician, vol 30 (no 4), pages 184-185. (on Sakai)
  
• Radwin, D. (October 5, 2009). “High response rates don’t ensure survey accuracy.” The Chronicle of Higher Education. (on Sakai)
  
• Ranganathan, M. (November 4, 2014), Where are the real errors in political polls?“, Scientific American.

Week 5 (elections)

9/27 Due (Monday at 11:59pm on Sakai): Essay 1

9/28 Context:Nico Discussion:Skylar Summary:Jacqueline

2020 election polling

• FiveThirtyEight is a poll aggregator https://projects.fivethirtyeight.com/polls/
• Alexander, J. (2010) “Appendix on the poll of polls.” The Performance of Politics. (on Sakai)
  
• Silver, N. (2014) “How FiveThirtyEight Calculates Pollster Ratings” FiveThirtyEight https://fivethirtyeight.com/features/how-fivethirtyeight-calculates-pollster-ratings/ and https://projects.fivethirtyeight.com/pollster-ratings/
  
• Final FiveThirtyEight prediction in 2020: https://projects.fivethirtyeight.com/2020-election-forecast/
  
• Cohn, N. (September 20, 2016), “We gave four good pollsters the same raw data. They had four different results.” The New York Times. https://www.nytimes.com/interactive/2016/09/20/upshot/the-error-the-polling-world-rarely-talks-about.html
• Keeter, S, Hatley, N, Lau, A, and Kennedy, C. (March 2, 2021) “What 2020’s Election Poll Errors Tell Us About the Accuracy of Issue Polling” Pew Research Center. https://www.pewresearch.org/methods/2021/03/02/what-2020s-election-poll-errors-tell-us-about-the-accuracy-of-issue-polling/

9/30 Peer review of Essay 1
9/30 Due (printed in class): completed peer reviews

Week 6 (decolonizing statistics)

10/5 Context: Jacqueline Discussion: Andrea Summary: Nick

meaning of “mathematics”

• Iseke-Barnes, J. (2000) “Ethnomathematics and Language in Decolonizing Mathematics.” Race, Gender, & Class, Vol 7, pgs 133-149. https://www.jstor.org/stable/41674951 (log in to library to see full paper)
• Su, F. (2015) “Mathematical Microaggressions.” MAA Focus. http://digitaleditions.walsworthprintgroup.com/publication/?i=278032&p=36

10/7 Context: Ty Discussion: Aja Summary: Mel

data and power

• D’Ignazio, C. and Klein, L. (2020) “The Power Chapter.” Data Feminism. https://data-feminism.mitpress.mit.edu/

10/8 Due (Friday, by 11:59pm to Sakai): Essay 1, after peer review

Week 7 (machine bias)

10/12 Context: Nick Discussion: Claire Summary: Andrea

consequences

• Castelvecchi, D. (November 18, 2020) “Is facial recognition too biased to be let loose?” Nature https://www.nature.com/articles/d41586-020-03186-4
• Simonite, T (2021) “What really happened when Google ousted Timnit Gebru.” Wired https://www.wired.com/story/google-timnit-gebru-ai-what-really-happened/
• Weinberger, S. (September 28, 2020) “Techie Software Soldier Spy Palantir, Big Data’s scariest, most secretive unicorn, is going public. But is its crystal ball just smoke and mirrors?”, New York Magazine https://nymag.com/intelligencer/2020/09/inside-palantir-technologies-peter-thiel-alex-karp.html
• MacCarthy (May 26, 2021) “Mandating fairness and accuracy assessments for law enforcement facial recognitionsystems.” Brookings https://www.brookings.edu/blog/techtank/2021/05/26/mandating-fairness-and-accuracy-assessments-for-law-enforcement-facial-recognition-systems/

10/14

10/15 Due (Friday, 11:59pm to Sakai): Micro-essay on Essay 2

algorithmic bias

• Coded Bias (2020). Shalini Kantayya, producer. 90 min. [will watch in class]

• [optional] O’Neil, C. (2016) Weapons of Math Destruction, pages 1-31, 84-104. (on Sakai)
  

• [optional] Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (May 23, 2016), “Machine bias.” ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  

• [optional] Hardesty, L. (February 11, 2018) “Study finds gender and skin-type bias in commercial artificial-intelligence systems” MIT News https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212

Week 8

10/19 Fall Break

10/21 Context: Mel Discussion: Arsh Summary: Claire

10/21 Due (Thursday, by 11:59pm, to Sakai): Essay 2

truth finding

• Elliott, K. (May 2, 2016) “39 studies about human perception in 30 minutes.” Medium. https://medium.com/@kennelliott/39-studies-about-human-perception-in-30-minutes-4728f9e31a73
• Tufte, E. (1997). Visual and Statistical Thinking: displays of evidence for making decisions. You can buy it here, but you can find it elsewhere: http://www.edwardtufte.com/tufte/books_textb [the reading is a pamphlet which is about 30 pages long. There are two stories: the Challenger explosion and John Snow’s work during the cholera epidemic. use Google to find it.]

Week 9 (data viz)

10/26 Peer review Essay 2

10/26 Due (printed in class): completed peer review

10/28 Context: Arsh Discussion: JC Summary: Aja

story telling

• Lupi, G. and Posavec, S. (2016) In particular, weeks: 1, 14, 17, 18, 22, 24, 39, & 52. Dear Data. http://www.dear-data.com/by-week/
  
• Mansky, J. (2018) “W.E.B. Du Bois’ Visionary Infographics Come Together for the First Time in Full Color” Smithsonian Mazagine, https://www.smithsonianmag.com/history/first-time-together-and-color-book-displays-web-du-bois-visionary-infographics-180970826/
• Weber, J. (2019) “How W.E.B. Du Bois meticulous visualized 20th-century black america.” Hyperallergic https://hyperallergic.com/476334/how-w-e-b-du-bois-meticulously-visualized-20th-century-black-america/

Week 10 (variables)

11/2 Due (Tuesday by 11:59pm, to Sakai): Essay 2, after peer review

11/2 Context: JC Discussion: Mel Summary: Arsh

other variable choices

• Adichie, N. (2009) “The danger of a single story.” TED https://www.ted.com/talks/chimamanda_ngozi_adichie_the_danger_of_a_single_story
• Dollar Street https://www.gapminder.org/dollar-street consider a few families - what similarities and difference do you notice?; choose one variable and see how it varies across the “street”.
  
• Jones, C.P. (2001) “Invited Commentary: ‘Race,’ Racism, and the Practice of Epidemiology.” American Journal of Epidemiolgoy, Vol 154, pgs 299-304. https://academic.oup.com/aje/article/154/4/299/61900
• Krause, H. (August 27, 2021) “We need to fill in the blanks in our social identity data.” We All Count https://weallcount.com/2021/08/27/we-need-to-fill-in-the-blanks-in-our-social-identity-data/
• Gupta, S. (March 8, 2020) To fight discrimination, the U.S. census needs a different race question. ScienceNews

11/4 Due: Dear Data presentations

your variable choices

Dear Data postcard (due by Wednesday 11/3 11:59pm to Sakai).
Some tips are here: https://fivethirtyeight.com/features/dear-data-and-fivethirtyeight-want-you-to-visualize-your-podcast-habits/

Week 11 (causation in medicine)

11/9 Context: Brendan Discussion: Kyle Summary: Alan

wellness programs

• O’Neil, C. (2016) Weapons of Math Destruction, pages 173-178. (on Sakai)
• Carroll, A. (August 6, 2018) “Workplace wellness programs don’t work well. Why some studies show otherwise.” The New York Times. https://www.nytimes.com/2018/08/06/upshot/employer-wellness-programs-randomized-trials.html

11/11 Context: Kyle Discussion: Alan Summary: Brendan

race vs. racism

• Boyd, R., Lindo, E., Weeks, L., and McLemore, M. (2020) “On Racism: a new standard for publishing on racial health inequalities.” Health Affairs Blog https://www.healthaffairs.org/do/10.1377/hblog20200630.939347/full/
  
• Vyas, D., Eisenstein, L., and Jones, D. (2020) “Hidden in plain sight — reconsidering the use of race correction in clinical algorithms.” The New England Journal of Medicine, vol 383, pgs 874-882. https://www.nejm.org/doi/full/10.1056/NEJMms2004740

11/11 Due (Thursday, 11:59pm to Sakai): Micro-essay on Essay 3

Week 12 (vaccines)

11/16 Context: Fiona Discussion: Nico Summary: Skylar

polio & MMR

• (polio) Meier P. (1989) The biggest public health experiment ever: the 1954 field trial of the Salk poliomyelitis vaccine. Statistics: A Guide to the Unknown, 3rd edition. The Joint Committee on the Curriculum in Statistics and Probability of the American Statistical Association and the National council of Teachers of Mathematics. Duxbury Press: Belmont, California. (on Sakai)
• (polio) Meldrum, M. (1998) “‘A calculated risk’: the Salk polio vaccine field trials of 1954.” British Medical Journal, vol 317, pgs 1233-1236. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1114166/
• (MMR) Sathyanarayana R., Andrade C. (2011) “The MMR vaccine and autism: Sensation, refutation, retraction, and fraud.” Indian J Psychiatry, vol 53, pgs 95-96. https://www.indianjpsychiatry.org/text.asp?2011/53/2/95/82529
• Carroll, A. (2014) “Vaccines Don’t Cause Autism” Healthcare Triage https://www.youtube.com/watch?v=o65l1YAVaYc
• Carroll, A. (2015) “Studies Confirm, Vaccines Still Don’t Cause Autism. But Are These Studies Helping?” Healthcare Triage https://www.youtube.com/watch?v=j_zqBPuPx8w

11/18 Context: Discussion: Summary:

efficacy

• Young-Saver, D. (December 13, 2020) “What does 95% effective mean? Teaching the math of vaccine efficacy.” The New York Times https://www.nytimes.com/2020/12/13/learning/what-does-95-effective-mean-teaching-the-math-of-vaccine-efficacy.html
• Morris, J. (August 17, 2021) “Israeli data: How can efficacy vs. severe disease be strong when 60% of hospitalized are vaccinated?”, https://www.covid-datascience.com/post/israeli-data-how-can-efficacy-vs-severe-disease-be-strong-when-60-of-hospitalized-are-vaccinated
• Doung-ngern, P. et al. (2020) “Case-Control Study of Use of Personal Protective Measures and Risk for SARS-CoV 2 Infection, Thailand.” Emerging Infectious Diseases, vol 26. https://wwwnc.cdc.gov/eid/article/26/11/20-3003_article

11/21 Due (Sunday by 11:59pm to Sakai): Annotated Bibliography for Essay 3

Week 13 (pandemics)

11/23 Context: Professor Hardin Discussion: Professor Hardin Summary: Professor Hardin

masks

• Markel, H., Lipman, H., and Navarro, J.A. et al. (2007) “Nonpharmaceutical interventions implemented by US cities during the 1918-1919 influenza pandemic” Journal of the American Medical Association, vol 298, pgs 644-654. https://jamanetwork.com/journals/jama/fullarticle/208354
  
• MacIntyre C. et al. (2015) “A cluster randomised trial of cloth masks compared with medical masks in healthcare workers.” Infection diseases, 5:e006577. https://bmjopen.bmj.com/content/5/4/e006577.short
• Doung-ngern, P. et al. (2020) “Case-Control Study of Use of Personal Protective Measures and Risk for SARS-CoV 2 Infection, Thailand.” Emerging Infectious Diseases, vol 26. https://wwwnc.cdc.gov/eid/article/26/11/20-3003_article

11/25 Thanksgiving

11/28 Due (Sunday by 11:59pm to Sakai): Essay 3

Week 14 (Statistics & Law)

11/30

• 12 Angry Men (1957), directed by Sidney Lumet, Orion-Nova Productions. https://www.imdb.com/title/tt0050083/ (in class)

12/2 Context: Discussion: Summary:

• 12 Angry Men
• Cobb, G. and Gehlbach, S. (2006). Statistics in the courtroom. Statistics: a Guide to the Unknown, 3rd edition. The Joint Committee on the Curriculum in Statistics and Probability of the American Statistical Association and the National council of Teachers of Mathematics. Duxbury Press: Belmont, California. (on Sakai)
• Gannon, M. (July 12, 2017) Amazing DNA Tool Gives Cops a New Way to Crack Cold Cases, nbcnews.com. https://www.nbcnews.com/mach/science/amazing-dna-tool-gives-cops-new-way-crack-cold-cases-ncna781946

Week 15 (fin)

12/7 Class presentations (OR posters: Wednesday 12/8 noon-2pm ????)

12/9 No class - reading days

12/10 Due (Friday by 11:59pm to Sakai): Essay 3, after peer review