Making HCI statistics better: saving the baby

Alan Dix

HCI Centre, University of Birmingham, UK
Talis, Birmingham, UK

Position paper for workshop Moving Transparent Statistics Forward @CHI 2017, May 6, 2017, Denver, USA

Download position paper (PDF, 74K)

I have long had a concern over the state of statistics in HCI, so welcome a chance to be part of this workshop. I will have a special interest in advancing statistical literacy and sharing of data and meta-data.

Keywords: Statistics; human–computer interaction; evaluation; hypothesis testing; Bayesian statistics.


Alan Dix is Professor at the University of Birmingham and Senior Researcher at Talis.  He is well known for his textbook and research in HCI and is a member of SIGCHI Academy. However, before he was in HCI, Alan was a mathematician, including representing the UK in the International Mathematical Olympiad.  In the past, he has practiced as a professional statistician and applied mathematician: including work on modelling agricultural crop sprays, medical statistics and undersea cable detection.  Within HCI these skills have been applied in his foundational work on formal methods for interactive systems, the use of Bayesian techniques in education, random sampling for visualisation of big data and uncertainty, and analysis of potential bias against human/applied areas in REF, the UK research assessment exercise. Alan is also running a course at CHI2017, "Making Sense of Statistics in HCI: From P to Bayes and Beyond".

Special areas of interest

I have worked in many areas of HCI including CSCW, mobile interfaces, technical creativity, and some of the earliest work on privacy and ethical implications of intelligent data processing. More recent work includes community engagement especially in rural areas and my one thousand mile research walk around Wales, which generated substantial quantitative and qualitative open research data from blogs to biodata.  In my Talis role I am interested on the use of technology in university-level education including learning analytics; that is where statistics, visualisation and big data techniques are applied within education.

Position Statement

The state of knowledge and practice of statistics in HCI has been a personal concern for many years from the standard mistakes, such as treating non-significant as no effect; to more subtle issues, such as confounding the success of a prototype system with that of the principles on which it is built.
However, there is a real danger of 'throwing the baby out with the bathwater', and I am equally alarmed by some of the hype surrounding new statistics and careless application such as confidence intervals being quoted without reference to the underlying statistical distributions and sample sizes used to construct them, and Bayesian statistics mixed with hypothesis testing in what appears to be cherry picking of techniques.

Part of the answer lies in better education (hence my statistics course at CHI this year and at CHI and other conferences some years ago), and part in better ways to structure and report methods and share data.

Potential Contribution

Following on from this, and linking to my broader interests in data, I was particularly interested in the discussions about systems for sharing of data and meta-data mentioned in "Proposal for advancing transparent statistics in HCI", and also how to establish educational or author/reviewer guidance materials.

In previous writing [1] I have argued that the data gathered in HCI should be valued and reported in its own right from methodology of collection to documentation of data formats.  Even if heavily anonymised or summarised, we should ideally have sufficient data published to allow alternative analyses.  In particular, the availability of original data for meta-analysis would avoid the need for Bayesian summary statistics [2] or post-hoc combination of p-values.



  1. Dix. A. 2010. Human-Computer Interaction: a stable discipline, a nascent science, and the growth of the long tail. Interacting with Computers, 22(1) pp. 13-27. papers/IwC-LongFsch-HCI-2010/.
  2. Kay, M., Nelson, G., and Hekler, E. 2016. Researcher-Centered Design of Statistics: Why Bayesian Statistics Better Fit the Culture and Incentives of HCI. CHI 2016, ACM, pp. 4521-4532. doi:10.1145/2858036.2858465

Alan Dix 19/2/2017