Week 1

9/4 No Class – Convocation

9/6 Introductory stuff, syllabus, webpage info

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

Week 2 (Against intuition / sports)

9/11 Discussion / Context / Summary: Professor Hardin
Some general thoughts on discussions

• Tversky, A. and Kahneman, D. (1974). “Judgment under uncertainty: heuristics and biases.” Science, vol 185, pgs 1124-1131. (https://library.claremont.edu/)
• Utts, J. (2005/2015). “Psychological influences on personal probability” (chp 16 in 4ed, chp 17 in 3ed). Seeing Through Statistics.

9/13 Discussion: Jacob / Context: Jared / Summary: Lydia

• Utts, J. (2005/2015). “When intuition differs from relative frequency” (chp 17 in 4ed, chp 18 in 3ed). Seeing Through Statistics.
  
• Berry, S. (2006) “Statistical fallacies in sports.” Chance, vol 19, pgs 50-56. (on Sakai)
  
• Lewis, M. (2003) Moneyball, pgs 66 - 73 and 272 - 275. (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)
• Ross, T. (June 25, 2015) “Welcome to smarter basketball.” The Atlantic. https://www.theatlantic.com/entertainment/archive/2015/06/nba-data-analytics/396776/

9/16 Due (Sunday, 11:59pm to Sakai): Micro-essay on believing/doubting game (based on next Tuesday’s reading!)

Week 3 (Fiction)

9/18 Discussion: Jared / Context: Keath / Summary: Vanessa

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

9/20 Discussion: Cameron / Context: Ethan / Summary: Jacob
9/20 Due (Thursday, 11:59pm to Sakai): Micro-essay for paper 1

• Dostoevsky, F. The Gambler. (Huntley)

Week 4 (Sampling)

9/25 Library Day
Due (bring with you on paper): library notes

9/27 Discussion: Will / Context: Lydia / Summary: Cathy

• [nothing to read here, we’ll do in class] Sampling applet: http://www.rossmanchance.com/applets/OneProp/OneProp.htm?candy=1

• Utts, J. (2005/2015) “How to get a good sample” (chp 4). Seeing Through Statistics.
  
• 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. https://blogs.scientificamerican.com/guest-blog/where-are-the-real-errors-in-political-polls/
  

• [skim don’t read:] Meng, X.-L. (2018) “Statistical Paradises and Paradoxes in Big Data: law of large populations, big data paradox, and the 2016 US presidential election.” The Annals of Applied Statistics. https://projecteuclid.org/download/pdfview_1/euclid.aoas/1532743473 [n.b., This article is very technical. You should skim it to get the basic ideas of statistical modeling to address issues of non-random sampling. Do not try to understand the technical details.]

Week 5 (2016 election)

10/1 Due (Monday at 11:59pm on Sakai): Paper 1

10/2 Discussion: Franco / Context: Cathy / Summary: Maelvi

• Final FiveThirtyEight prediction: https://projects.fivethirtyeight.com/2016-election-forecast/
  
• 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/
  
• Cohn, N. (October 12, 2016) “How one 19-Year-Old Illinois man is distorting national polling averages.” The New York Times. https://www.nytimes.com/2016/10/13/upshot/how-one-19-year-old-illinois-man-is-distorting-national-polling-averages.html
  
• 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
  
• [AND THE FOLLOW UP] Gelman, A. (September 23, 2016) “Trump +1 in Florida; or, a quick comment on that ‘5 groups analyze the same poll’ exercise.” http://andrewgelman.com/2016/09/23/trump-1-in-florida-or-a-quick-comment-on-that-5-groups-analyze-the-same-poll-exercise/
• [NOTE THE DATE HERE] Silver, N. (October 24, 2016) “Election update: why our model is more bullish than others on Trump.” FiveThirtyEight http://fivethirtyeight.com/features/election-update-why-our-model-is-more-bullish-than-others-on-trump/
• [NOTE THE DATE HERE] Grim, R. (November 5, 2016) “Nate Silver is Unskewing Polls - All of Them - In Trump’s Direction” HuffPost https://www.huffingtonpost.com/entry/nate-silver-election-forecast_us_581e1c33e4b0d9ce6fbc6f7f
  
• Silver, N. (November 8, 2016) “Final election update: There’s a wide range of outcomes, and most of them come up Clinton.” FiveThiryEight. https://fivethirtyeight.com/features/final-election-update-theres-a-wide-range-of-outcomes-and-most-of-them-come-up-clinton/

10/4 Peer review 10/4 Due (printed in class): completed peer review

Week 6 (Models for 2018 / Decolonizing Statistics)

10/9 Discussion: Vanessa / Context: Cameron / Summary: Chris

• [Toggle at the top to see different genres, scroll down to see individual races] https://projects.fivethirtyeight.com/2018-midterm-election-forecast/house/
  

• [THEIR METHODS!] Silver, N. (August 16, 2018) “How FiveThirtyEight’s House Model Works.” FiveThirtyEight. https://fivethirtyeight.com/features/2018-house-forecast-methodology/

• Pew Research Center (August 9, 2018) “For most Trump voters, ‘Very Warm’ feelings for him endured.” http://assets.pewresearch.org/wp-content/uploads/sites/5/2018/08/13090439/8-9-2018-Validated-voters-release1.pdf
  
• [Just read the executive summary] American Association for Public Opinion Research “An evaluation of 2016 election polls in the U.S.” https://www.aapor.org/Education-Resources/Reports/An-Evaluation-of-2016-Election-Polls-in-the-U-S.aspx
  
• Gelman, A. and Azari, J. (2017) “19 things we learned from the 2016 election.” Statistics and Public Policy, vol 4, pages 1-10. https://www.tandfonline.com/doi/full/10.1080/2330443X.2017.1356775
  

• Barbaro, M. (August 10, 2018) “The Trump voters we don’t talk about” The Daily. https://radiopublic.com/TheDaily/ep/s1!3abf8

and don’t miss NYT LIVE ELECTION POLLING!!! Does it get any more fun than that????…

• https://www.nytimes.com/interactive/2018/upshot/elections-polls.html?module=inline
  
• [Description] https://www.nytimes.com/2018/09/06/upshot/midterms-2018-polls-live.html
  
• [Pros and Cons, etc.] https://www.nytimes.com/2018/09/06/upshot/live-poll-explainer.html?module=inline

10/11 Discussion: Lydia / Context: Vanessa / Summary: Cameron
10/12 Due (Friday, by 11:59pm to Sakai): Paper 1, after peer review

• Champkin, J. (January 24, 2014) “The timeline of statistics.” Significance. https://www.statslife.org.uk/history-of-stats-science/1190-the-timeline-of-statistics
• Iseke-Barnes, J. (2000) “Ethnomathematics and Language in Decolonizing Mathematics.” Race, Gender, & Class, Vol 7, pgs 133-149. https://www.jstor.org/stable/41674951

Week 7 (Decolonizing Statistics / Reproducibility)

10/16 Discussion: Ethan / Context: Will / Summary: Jared
10/16 Due (Tuesday, 11:59pm to Sakai): Micro-essay on Paper 2

• Su, F. (2015) “Mathematical Microaggressions.” MAA Focus. http://digitaleditions.walsworthprintgroup.com/publication/?i=278032&p=36

• Steele, C. (1997) “A Threat in the Air: How Stereotypes Shape Intellectual Identity and Performance.” American Psychologist, vol 52 (6), pgs 613-629.

  • The article I’ve assigned is long, alas - I chose it because it is the most complete summary of his work that I can find, and it does a great job of explaining the nuances of the argument, along with summarizing some really compelling research studies. Since the paper is long, consider the following questions, to help focus your reading (thanks to Janice Hudgings in Physics for these suggestions):

    1. What precisely is stereotype threat, and how does it differ from implicit bias, self-doubt, and explicit biases such as socioeconomic and educational discrimination, which also can affect educational success? The paper repeatedly makes the point that stereotype threat is NOT referring to the idea that negative stereotypes impair women and/or people of color’s academic performance by triggering self-doubt about their own abilities – so, what then is stereotype threat?
       
    2. Beyond the two examples of stereotype threat that Dr. Steele is discussing in his paper, what are some other examples of stereotype threat?
       
    3. What are the main experiments that Dr. Steele’s research group did to test their ideas about stereotype threat?

10/18 Discussion: Brandon / Context: Jacob / Summary: Franco

• Peng, R. (June 6, 2014) “The real reason reproducible research is important.” simplystatistis.org https://simplystatistics.org/2014/06/06/the-real-reason-reproducible-research-is-important/
  
• Bialik, C. (May 20, 2015) “As a major retraction shows, we’re all vulnerable to faked data.” FiveThirtyEight. https://fivethirtyeight.com/features/as-a-major-retraction-shows-were-all-vulnerable-to-faked-data/
  
• Baumer, B., Kaplan, D., Horton, N. (2017) “Professional ethics.” Modern Data Science with R. http://mdsr-book.github.io/excerpts/mdsr-ethics.pdf (not section 6.2 … we will cover graphs in a few weeks)

Week 8

10/23 Fall Break 10/23 Due (Tuesday, by 11:59pm, to Sakai): Paper 2

10/25 Peer review 10/25 Due (printed in class): completed peer review

Week 9 (Machine Bias)

10/30 Discussion: Andriw / Context: Brandon / Summary: Keath

• O’Neil, C. (2016) Weapons of Math Destruction, pages 1-31, 84-104. (on Sakai)
  
• 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 Reading, slightly technical] Angwin, J. and Larson, J. (December 30, 2016) “Bias in criminal risk scores is mathematically inevitable, researchers say.” ProPublica. https://www.propublica.org/article/bias-in-criminal-risk-scores-is-mathematically-inevitable-researchers-say
  
• [Optional reading, maybe repetitive?] Alexander, M. (Nov 8, 2018) “The Newest Jim Crow.” The New York Times. https://www.nytimes.com/2018/11/08/opinion/sunday/criminal-justice-reforms-race-technology.html

11/1 Discussion: Maelvi / Context: Andriw / Summary: Brandon

• 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/
• 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.]

11/4 Due (Sunday by 11:59pm, to Sakai): Paper 2, after peer review

Week 10 (Human Perception of Graphs)

11/6 Discussion: Chris / Context: Franco / Summary: Andriw

• 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
• Utts, J. (2005/2015). “Plots, graphs, and pictures” (chp 9). Seeing Through Statistics. (Huntley)
• Huff, D. (1982). “The gee-whiz graph & the one dimensional picture.” How to Lie with Statistics. (on Sakai)

11/8

• Dear Data presentations
• What is going on in this graph? https://www.nytimes.com/column/whats-going-on-in-this-graph

Due: this week you should be working on your data postcards
Dear Data postcard (due by Wednesday 11/7 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 / Ethics in Medicine)

11/13 Discussion: Keath / Context: Samuel / Summary: Ethan

• Utts, J. (2005 /2015) “Experiments and observational studies” (chp 5). Seeing Through Statistics.
• 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

11/15 Discussion: Samuel / Context: Maelvi / Summary: Will
11/15 Due (Thursday, 11:59pm to Sakai): Micro-essay on paper 3

• 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. (Sakai)
• Altman, D. (1980) “Statistics and ethics in medical research. Misuse of statistics is unethical.” British Medical Journal. vol 281, pgs 1182-1184. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1714517/pdf/brmedj00045-0026.pdf
  
• Lee, S. “Here’s How Cornell Scientist Brian Wansink Turned Shoddy Data into Viral Studies about how we Eat.” Buzzfeednews.com https://www.buzzfeednews.com/article/stephaniemlee/brian-wansink-cornell-p-hacking
  
• [Optional, very technical, about p-values] Gelman, A. ad Loken, E. “The garden of forking paths: Why multiple comparisons can be a problem, even when there is no ‘fishing expedition’ or ‘p-hacking’ and the research hypothesis was posited ahead of time.” White paper. http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf

Week 12 (Medical Conclusions)

11/20 Discussion: Cathy / Context: Chris / Summary: Samuel

Week 13 (Medicine)

11/27 Discussion: / Context: / Summary:

• quality of life, how to measure, Dear Data

• Harmon A. (18 September 2010). New Drug Stirs Debate on Rules of Clinical Trials. The New York Times http://www.nytimes.com/2010/09/19/health/research/19trial.html?hp
• Rabine, R. 24 September 2018. NYT Online Trial by Fire: Critics Demand that a Huge Sepsis Study Be Stopped. https://www.nytimes.com/2018/09/24/health/sepsis-trial-treatments.html
• Gawande A. (2010). Letting Go. Annals of Medicine. The New Yorker. 2 August 2010. http://www.newyorker.com/reporting/2010/08/02/100802fa_fact_gawande

• Gould SJ. The median isn’t the message. CancerGuide: Statistics. (on Sakai)

11/29

• 12 Angry Men

12/2 Due (Sunday by 11:59pm to Sakai): Paper 3

Week 14 (Statistics & Law)

12/4 Discussion: / Context: / Summary: Chris

• 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)
• [Read to get a basic idea of how statistics is used for hypothesis testing. Don’t try to understand every detail.] Utts, J. (2005). Chapters 22, 23, 24. Seeing Through Statistics. (Huntley)

12/6 Discussion: / Context: / Summary: Chris

• Weir, B. (2006). DNA Fingerprinting. Statistics: a Guide to the Unknown. (on Sakai)
  
• (May 28, 1995) The Power of DNA Evidence, New York Times. https://www.nytimes.com/1995/05/28/opinion/the-power-of-dna-evidence.html
  
• Greenwood, V. (July 2016) How Science is Putting a New Face on Crime Solving. National Geographic Magazine. https://www.nationalgeographic.com/magazine/2016/07/forensic-science-justice-crime-evidence/
  
• 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
  
• Neuborne, B. (Nov 29, 2018) “Trum may fire Mueller, but he can’t fire Mueller’s grand jury” LA Times. https://www.latimes.com/opinion/op-ed/la-oe-neuborne-mueller-grand-juries-20181129-story.html

Week 15

12/11 Class presentations (OR posters: Wednesday 12/12 noon-2pm ????)
12/14 Due (Friday by 11:59pm to Sakai): Paper 3, after peer review

12/13 Reading Days