Alan Dix
ABSTRACT
Many researchers and practitioners find statistics confusing. This course aims to give attendees an understanding of the meaning of the various statistics they see in papers or need to use in their own work. The course builds on the instructor’s previous tutorials and master classes including at CHI 2022, and on his recently book “Statistics for HCI: Making Sense of Quantitative Data”. The course will focus especially on material you will not find in a conventional textbook or statistics course including aspects of statistical ‘craft’ skill, and offer attendees an introduction to some of the instructor’s extensive online material.
- Monya Baker. 2016. Statisticians issue warning over misuse of P values. Nature News 531, 7593 (March 2016), 151. https://doi.org/10.1038/nature.2016.19503Google Scholar
Cross Ref
- Andy Cockburn, Carl Gutwin, and Alan Dix. 2018. HARK No More: On the Preregistration of CHI Experiments. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3173574.3173715Google ScholarDigital Library
- Alan Dix. 2003. Mastery. SIGCHI Bull.: Suppl. Interactions 2003 (March 2003), 7. https://doi.org/10.1145/967199.967209Google ScholarDigital Library
- Alan Dix. 2020. Statistics for HCI: Making Sense of Quantitative Data. Morgan & Claypool. https://doi.org/10.2200/S00974ED1V01Y201912HCI044Google ScholarCross Ref
- John P. A. Ioannidis. 2005. Why Most Published Research Findings Are False. PLoS Med 2, 8 (08 2005), e124. https://doi.org/10.1371/journal.pmed.0020124Google ScholarCross Ref
- Maurits Kaptein and Judy Robertson. 2012. Rethinking Statistical Analysis Methods for CHI. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Austin, Texas, USA) (CHI ’12). Association for Computing Machinery, New York, NY, USA, 1105–1114. https://doi.org/10.1145/2207676.2208557Google ScholarDigital Library
- Matthew Kay, Gregory L. Nelson, and Eric B. Hekler. 2016. Researcher-Centered Design of Statistics: Why Bayesian Statistics Better Fit the Culture and Incentives of HCI. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 4521–4532. https://doi.org/10.1145/2858036.2858465Google ScholarDigital Library
Index Terms
- Statistics for HCI
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