Fair Statistical Communication in HCI

  • Pierre DragicevicEmail author
Part of the Human–Computer Interaction Series book series (HCIS)


Statistics are tools to help end users accomplish their task. In research, to be qualified as usable, statistical tools should help researchers advance scientific knowledge by supporting and promoting the effective communication of research findings. Yet areas such as human-computer interaction (HCI) have adopted tools — i.e., p-values and dichotomous testing procedures — that have proven to be poor at supporting these tasks. The abusive use of these procedures has been severely criticized in a range of disciplines for several decades, suggesting that tools should be blamed, not end users. This chapter explains in a non-technical manner why it would be beneficial for HCI to switch to an estimation approach, i.e., reporting informative charts with effect sizes and interval estimates, and offering nuanced interpretations of our results. Advice is offered on how to communicate our empirical results in a clear, accurate, and transparent way without using any tests or p-values.


Confidence Interval Credible Interval Interval Estimate Sampling Variability Usability Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Many thanks to Elie Cattan, Fanny Chevalier, Geoff Cumming, Steven Franconeri, Steve Haroz, Petra Isenberg, Yvonne Jansen, Maurits Kaptein, Heidi Lam, Judy Robertson, Michael Sedlmair, Dan Simons, Chat Wacharamanotham and Wesley Willett for their helpful feedback and comments.


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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Orsay CedexFrance

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