Statistics for HCI. Alan Dix
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8.2.2 Correlated features
8.5 Simulation and empirical methods
8.6 What you can say—phenomena and statisticians
9 Differences and distinctions
9.1.1 What do we know about the world?
PART III Design and Interpretation
10 Gaining power –the dreaded ‘too few participants’
10.1 If there is something there, make sure you find it
10.2 The noise–effect–number triangle
10.3.1 More subjects or trials (increase number)
10.3.2 Within-subjects/within-groups studies (reduce noise)
10.3.3 Matched users (reduce noise)
10.3.4 Targeted user group (increase effect)
10.4.1 Distractor tasks (increase effect)
10.4.2 Targeted tasks (increase effect)
10.4.3 Demonic interventions! (increase effect)
10.4.4 Restricted tasks (reduce noise)
11 So what? —making sense of results
11.1.1 Fitts’ Law—jumping to the numbers
11.3 What have you really shown?
11.3.1 Think about the conditions
11.3.2 Individual or the population
11.4 Diversity: individual and task
11.4.1 Don’t just look at the average
11.5.1 Quantitative and statistical meet qualitative and theoretical
11.5.3 Example: mobile font size
11.6.1 Repeatability and replication
11.6.2 Meta-analysis and open scholarship
12 Moving forward: the future of statistics in HCI