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# Making Sense of Statistics in HCI: From P to Bayes and Beyond – introduction

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Many find statistics confusing, and perhaps more so given recent publicity of problems with traditional p-values and alternative statistical techniques including confidence intervals and Bayesian statistics. This course aims to help attendees navigate this morass: to understand the debates and more importantly make appropriate choices when designing and analysing experiments, empirical studies and other forms of quantitative data.

Published in: Data & Analytics
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### Making Sense of Statistics in HCI: From P to Bayes and Beyond – introduction

1. 1. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix Making Sense of Statistics in HCI: From P to Bayes and Beyond Alan Dix http://alandix.com/statistics/
2. 2. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix [-0.1,+3.2] [ 0.2, 3.7 ] 95% conf. int. p-values, confidence intervals, Bayesian stats xxx what does it all mean?
3. 3. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix confused?
4. 4. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix focus on understanding concepts and ideas
5. 5. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix make the most of your empirical effort and avoid misleading results
6. 6. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix why is probability so hard? subconscious behavioural conditioning needs lots and lots of exposures associative and semi-probabilistic conscious thinking and learning learn from single example single model of the world … but probability hard * see also: http://gladwell.com/blink/the-second-mind/ https://archive.org/details/controllingbehaviorthroughreinforcement
7. 7. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix why is statistics so hard? some maths need to understand both real world
8. 8. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix the ‘real’ world the sample actual measured data the population large set from which the data is drawn especially for surveys etc. the ideal the ‘typical’ user, the fair coin unrepeatable events – the fall of a raindrop a theoretical distribution
9. 9. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix the job of statistics
10. 10. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix overview – four parts wild and wide exploring randomness, uncertainty and 'distributions’ doing it alternative statistical analyses: the ubiquitous 'p' to Bayesian gaining power avoid the dreaded 'too few participants’ so what? making sense of your data and avoiding the pitfalls
11. 11. Making Sense of Statistics in HCI: From P to Bayes and Beyond – Alan Dix more … various things including Javascript demos at: http://alandix.com/statistics/ any updates of these materials, or further information for CHI course attendees at: http://alandix.com/statistics/course/chi2017/