14.1  Look at the data

14.1.1  Fitts’ Law – jumping to the numbers

14.1.2  But I did a regression

14.2  Visualise carefully

14.2.1  Choice of baseline

14.2.2  Choice of basepoint

14.3  What have you really shown?

14.3.1  Think about the conditions

14.3.2  Individual or the population

14.3.3  System vs properties

14.3.4  What went wrong?

14.4  Diversity: individual and task

14.4.1  Don’t just look at the average

14.4.2  Tasks too

14.5  Mechanism

14.5.1  Quantitative and statistical meet qualitative and theoretical

14.5.2  Generalisation

14.5.3  Example: mobile font size

14.6  Building for the future

14.6.1  Repeatability and replication

14.6.2  Meta-analysis and open scholarship

ACM conference, ANOVA (Analysis of Variance), base period, baseline, basepoint, borrowed methods, CHI conference, claims analysis, constant area visualisation, controlled environments, controlled experiment, data analyst, data consumer, data journals, day effects, dependent variable, design space, ecological validity, effect size, emergent patterns, empirical data, error rate, expert evaluation, extrapolation, extreme values, false baseline, false origin, file tree visualisation, fine positioning tasks, Fitts' Law, fixed effect, font size, generative artefact, in-the-wild, independent variable, index of difficulty (IoD), inter-related factors, interpolate, linear regression, mechanism, menu navigation, meta-analysis, model, Monte Carlo, multiple causes, open scholarship, open situations, p-value, Pearson correlation coefficient, peripheral vision, PieTree, pointing task, public domain, public sector borrowing, qualitative methods, quantitative data, R, random effect, repeatability, replication, RepliCHI, response time, sample-based approaches, scatter plots, sensitivity, significant result, single cause, SPSS, statistical analysis, statistical significance, Student's t-test, subjects, target size, task completion time, tasks, temporal data, theoretical understanding, TreeMap, variability, visualisation