Editing CommunityData:StatsGaps (section)

=== Week 6: Categorical data ===

All:
* Gelman, Andrew and Eric Loken. 2014. “The Statistical Crisis in Science Data-Dependent Analysis—a ‘garden of Forking Paths’—explains Why Many Statistically Significant Comparisons Don’t Hold Up.” ''American Scientist'' 102(6):460. [[https://www.americanscientist.org/issues/pub/2014/6/the-statistical-crisis-in-science/1 Available through Library Subscription]] (This is a reworked version of [http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf this unpublished manuscript] which provides a more detailed examples.) Also note the correction here: https://statmodeling.stat.columbia.edu/2014/10/14/didnt-say-part-2/

Learn R:
*[https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w06-R_lecture.Rmd Week 6 R lecture materials] (.Rmd file)

Learn Stats:
* Read Diez, Barr, and Çetinkaya-Rundel: §6.1-6.4 (Inference for categorical data). 
* Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in ''Proceedings of the 8th ACM Conference on Designing Interactive Systems.'' Aarhus, Denmark: ACM. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on Hill's personal website]]
* Do [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 6]]

Refresh and get Stronger:
* Skim Diez, Barr, and Çetinkaya-Rundel: §6.1-6.4 (Inference for categorical data). 
* Read over [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 6]]

'''Resources'''
* Diez, Barr, and Çetinkaya-Rundel: §6.5-6.6 (Small samples and randomization inference)
* Verzani: §3.4 (Bivariate categorical data); §10.1-10.2 (Goodness of fit)
* [https://www.openintro.org/download.php?file=os3_slides_06&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §6 Lecture Notes]
* [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 4 videos for §7