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So You Think You Can Do Statistics: A Course in Statistical Thinking
Readings and Homework (Example Syllabus)
SECTION I: MAKING SENSE OF DATA
Week 1 – How the human brain works, scientific reasoning, and simulation thinking
Introductory Readings:
Tversky, A. and Kahneman, D., 1974. Judgment under uncertainty: Heuristics and biases. Science, 185(4157), pp.1124-1131.
Wainer, H., 2007. The most dangerous equation. American Scientist, 95(3), p.249.
Tu, Y.K. and Gilthorpe, M.S., 2007. The most dangerous hospital or the most dangerous equation?. BMC health services research, 7(1), p.185.
http://www.gatesfoundation.org/Media-Center/Press-Releases/2003/04/Oregon-Small-Schools-Initiative
Written Homework:
Write a short paragraph(s) explaining what the kidney cancer exercise, the Tversky and Kahnemen article, and dangerous hospitals have to do with one another.
Write up a quick example of a time when you made an assumption or decision based on one of the heuristics or fallacies from Tversky and Kahnemen.
Week 2 – Coincidences, randomness, and measurement (and an introduction to R)
Wrap-Up Watching:
“Thinking, Fast and Slow by Daniel Kahneman ; Animated Book Summary” - https://www.youtube.com/watch?v=vXVBL7UDOk4
Written Homework:
Create a short video or write a short blog post using the same tone as the Thinking Fast and Slow book summary to explain the fallacy and truth of the Kidney Cancer exercise.
Write a short paragraph to explain why you think we chose these topics for the first week of a course like this
Using the code developed in class simulate and graph results for (a) sample size of 14 (b) sample size of 56 (c) success = exactly 50% red and sample size = 4 (d) exactly 50% red and n = 8. Write a few sentences about what the simulations demonstrated.
Introductory Readings:
http://www.toptenz.net/top-10-most-remarkable-coincidences-in-history.php
Diaconis, P. and Mosteller, F., 2006. Methods for studying coincidences (pp. 605-622). Springer New York.Haggstrom
https://www.psychologytoday.com/blog/reality-play/201207/being-amused-apophenia
http://www.scientificamerican.com/article/patternicity-finding-meaningful-patterns/
Week 3 – Populations, samples, means, confidence Intervals, and over-confidence
Wrap-up Readings:
Olle Haggstrom paper: Why the Empirical Sciences Need Statistics So Desperately http://www.math.chalmers.se/~olleh/haggstrom_proc_ems.pdf
Introductory Readings and Watchings:
Changing math education by Arthur Benjamin: https://www.ted.com/talks/arthur_benjamin_s_formula_for_changing_math_education?language=en
Gawande, Atul. 2016. The Mistrust of Science, The New Yorker. June 10, 2016. http://www.newyorker.com/news/news-desk/the-mistrust-of-science
Sutherland WJ, Spiegelhalter D, Burgman MA. 2013. Twenty tips for interpreting scientific claims. Nature 503: 335-337.
Explore the R Graph Gallery: http://www.r-graph-gallery.com/
Written Homework:
R exercises distributed in class
Submit an amazing graphic display (a graph, an interactive display etc) and explain what it shows and what you find impressive about it.
Come to class prepared to (1) state which of the 20 tips in Sutherland et al. we've covered (in some way or another) so far, (2) provide an example of how one of these tips could help you interpret something from the news or media that you've read or been aware of in the past, and (3) which tips you don't understand.
SECTION II: USING DATA FOR SCIENTIFIC UNDERSTANDING
Week 4 – Comparing two populations
Introductory Readings:
Play with this website for 10 minutes: http://guessthecorrelation.com/
Steel EA, Kennedy MC, Cunningham PG, Stanovick JS. 2013. Applied statistics in ecology: common pitfalls and simple solutions. Ecosphere 4: 115. https://onlinelibrary.wiley.com/doi/10.1890/ES13-00160.1/full (Introduction and first 4 tips)
Coe R. 2002. It’s the Effect Size, Stupid: What effect size is and why it is so important. Available at: https://cebma.org/wp-content/uploads/Coe-2002.pdf
Extra4Experts: Explore these slides on Permutation Tests: http://faculty.washington.edu/kenrice/sisg/SISG-08-06.pdf
Written Homework:
R exercises: Bootstrapping confidence intervals (graphing)
Week 5 – The life cycle of science
Written homework:
R exercises: Effect sizes
R exercises: Permutation tests
Introductory Readings:
Nuzzo R. 07 October 2015. How scientists fool themselves – and how they can stop. Nature. http://www.nature.com/news/how-scientists-fool-themselves-and-how-they-can-stop-1.18517
Zuur AF, Leno EN, Elphick CS. 2010. A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution 1: 3-14. https://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2009.00001.x/full
Bohannon DK, Homm P, Alexander Driehaus J. 2015. Chocolate with high cocoa content as a weight-loss accelerator. Global Journal of Medical Research, 15: 2.
Week 6 – P-Values and Power
Wrap-Up Readings:
https://io9.gizmodo.com/i-fooled-millions-into-thinking-chocolate-helps-weight-1707251800
Written Homework:
R exercises: P-values
Introductory Readings and Watchings:
Belluz J, Plumer B, Resnick B. September 7 2016. The 7 biggest problems facing science, according to 270 scientists. VOX. http://www.vox.com/2016/7/14/12016710/science-challeges-research-funding-peer-review-process
TrafimowD, Marks M. 2015. Editorial. Basic and Applied Social Psychology 37:1-2. https://www.tandfonline.com/doi/abs/10.1080/01973533.2015.1012991?journalCode=hbas20
Wasserman RL, Lazar NA. 2016. The ASA’s Statement on p-Values: Context, Process, and Purpose. The American Statistician 70: 129-133. https://amstat.tandfonline.com/doi/pdf/10.1080/00031305.2016.1154108
https://simplystatistics.org/posts/2016-02-01-a-menagerie-of-messed-up-data-analyses-and-how-to-avoid-them/
Watch: https://www.youtube.com/watch?v=0Rnq1NpHdmw
SECTION III: USING SCIENCE IN SOCIETY
For Week 7 – Study design and science (statistical) communication
Wrap-Up Readings:
Forstmeier W, Wagenmakers EJ, Parker TH. 2016. Detecting and avoiding likely false-positive findings _ a practical guide. Biological Reviews. https://onlinelibrary.wiley.com/doi/10.1111/brv.12315/epdf
https://andrewgelman.com/2017/03/09/preregistration-like-random-sampling-controlled-experimentation/
Written Homework:
R exercises: statistical power
Estimate the proportion of drivers on their cell phones. Details specifically not provided. Just come to class with an estimate
Evaluate the article Dads Matter and write a short list of things that were done well and things that need to be improved.
Introductory Readings:
https://mathwithbaddrawings.com/2013/12/02/headlines-from-a-mathematically-literate-world/
McMillen M. October 2016. Dads Matter. WebMD.com.
Jung, K. et al. 2014. Female hurricanes are deadlier than male hurricanes. PNAS 111:8782-8787
RESOURCE: http://www.nwcphp.org/training/opportunities/webinars/using-slides-effectively-in-presentations (To watch this, scroll to Slides (Overview tab) and click “PH LearnLink” then register for a free account. Confirm your account by clicking on the e-mail. That will take you to a generic page at PH LearnLink. Go back to this link and click through again, hit “Enroll” and watch.
Week 8 – Statistical and science application: Big data
Wrap-up Readings:
https://www.washingtonpost.com/blogs/monkey-cage/wp/2014/06/05/hurricanes-vs-himmicanes/
https://andrewgelman.com/2014/06/06/hurricanes-vs-himmicanes/
Introductory Readings:
Kearney MS, Levine PB. 2014. Media Influences on Social Outcomes: The Impact of MTV's 16 and Pregnant on Teen Childbearing, NBER Working Paper No. 19795: https://www.nber.org/papers/w19795
One of the following:
Boyd D, Crowford K. 2011. Six provocations for Big Data. Paper to be presented at the Oxford Internet Institute’s “A Decade in Time: Symposium on the Dynamics of the Internet and Society.” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431
https://timharford.com/2014/04/big-data-are-we-making-a-big-mistake/
https://www.newsweek.com/2014/08/01/big-data-may-not-be-all-its-cut-out-be-260909.html
Written Homework:
Write up a short summary of the types of data used by the researchers studying media influences on social outcomes and how they tried to correct for biases. What might you add or do differently to explore these data further?
Week 9 – Statistical and science application: Climate change
Written Homework:
Write up Google Trends exercise from Thursday’s Lab
Introductory Readings:
Climate, science, belief, media: http://www.npr.org/templates/story/story.php?storyId=124008307
Skim Climate Change 2013; The physical Science Basis: Summary for Policymakers: https://www.stat.washington.edu/peter/498.Sp16/IPCC.pdf
Preparation for Thursday Lab:
Guttorp P, Kim TY 2013. Journal of Climate 26:6323-6328. Uncertainty in ranking the hottest years of US surface temperatures. https://www.stat.washington.edu/peter/498.Sp16/uncertainty.pdf
Code for that paper: https://www.statmos.washington.edu/wp/wp-content/uploads/2012/10/Uncertainty-analysis.txt
Lab: https://www.stat.washington.edu/peter/498.Sp16/Climate%20ranking%20uncertainty.htm
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