course

Statistics for HCI

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Alan DixAuthors Info & Claims

CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 141, Pages 1 - 4

https://doi.org/10.1145/3411763.3445018

Published: 08 May 2021 Publication History

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Abstract

Many researchers and practitioners find statistics confusing. This course aims to help change that, 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 2017, and on his recently published 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 it will also offer the attendees an introduction to some of the instructor’s extensive additional online material.

References

[1]

Monya Baker. 2016. Statisticians issue warning over misuse of P values. Nature News 531, 7593 (March 2016), 151. https://doi.org/10.1038/nature.2016.19503

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[2]

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/10.1145/3173574.3173715

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[3]

Alan Dix. 2003. Mastery. SIGCHI Bull.: Suppl. Interactions 2003 (March 2003), 7. https://doi.org/10.1145/967199.967209

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[4]

Alan Dix. 2020. Statistics for HCI: Making Sense of Quantitative Data. Morgan & Claypool.

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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.0020124

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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/10.1145/2207676.2208557

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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/10.1145/2858036.2858465

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Published In

CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

2965 pages

ISBN:9781450380959

DOI:10.1145/3411763

Editors:
Yoshifumi Kitamura
Tohoku University, Japan
,
Aaron Quigley
University of New South Wales, Australia
,
Katherine Isbister
University of California Santa Cruz, USA
,
Takeo Igarashi
The University of Tokyo, Japan

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2021

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Refereed limited

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CHI '21

Sponsor:

SIGCHI

CHI '21: CHI Conference on Human Factors in Computing Systems

May 8 - 13, 2021

Yokohama, Japan

Acceptance Rates

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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