Statistics for HCI. Alan Dix

8.2.2 Correlated features

       8.3 Everything is random

       8.4 The same or worse

       8.4.1 Everything is unlikely

       8.4.2 Numeric data

       8.4.3 More complex ‘or worse’

       8.4.4 Post-hoc corrections

       8.5 Simulation and empirical methods

       8.6 What you can say—phenomena and statisticians

       9 Differences and distinctions

       9.1 Philosophical differences

       9.1.1 What do we know about the world?

       9.1.2 Not so different

       9.2 So which is it?

       9.2.1 The statistical crisis

       9.2.2 Alternative statistics

       9.3 On balance (my advice)

       9.4 For both

       9.5 Endnote

       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

       10.2.1 General strategies

       10.3 Subjects

       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 Tasks

       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 Look at the data

       11.1.1 Fitts’ Law—jumping to the numbers

       11.1.2 But I did a regression

       11.2 Visualise carefully

       11.2.1 Choice of baseline

       11.2.2 Choice of basepoint

       11.3 What have you really shown?

       11.3.1 Think about the conditions

       11.3.2 Individual or the population

       11.3.3 System vs. properties

       11.3.4 What went wrong?

       11.4 Diversity: individual and task

       11.4.1 Don’t just look at the average

       11.4.2 Tasks too

       11.5 Mechanism

       11.5.1 Quantitative and statistical meet qualitative and theoretical

       11.5.2 Generalisation

       11.5.3 Example: mobile font size

       11.6 Building for the future

       11.6.1 Repeatability and replication

       11.6.2 Meta-analysis and open scholarship

       12 Moving forward: the future of statistics in HCI

       12.1 Positive changes

       12.2 Worrying trends

       12.3 Big data and machine learning

       12.4 Last words

       Bibliography

       Author’s