1.1  Why are probability and statistics so hard?

1.1.1  In two minds

1.1.2  Maths and more

1.2  Do you need stats at all?

1.3  The job of statistics – from the real world to measurement and back again

1.3.1  The ‘real’ world

1.3.2  There and back again

1.3.3  Noise and randomness

1.4  Why are you doing it?

1.4.1  Empirical research

1.4.2  Software development

1.4.3  Parallels

1.5  What’s next

A–B test, Bayesian reasoning, Bayesian statistics, bias, big data, Binomial distribution, cherry picking, coin tossing, confidence interval, conscious thinking, effect size, empirical, empirical data, empirical methods, epistemologically random, error bars, error rate, estimate, expert evaluation, explanation, exploration stage, fair coin, five users (the myth), formalist, formative evaluation, formative stages, frequentist, good-enough solution, hypothesis testing, idealist, in-the-wild, independence, iterative development, iterative evaluation, job of statistics, mathematics, mathematics of probability, mechanism, Normal distribution, Normal distribution, odds ratio, ontologically random, p-value, plausibility, population, probabilistic phenomena, probability distribution, qualitative methods, R, random effect, real world, reasonable expectation, sample, Schrodinger's cat, seeded, simulation methods, software development, software development process, statistical analysis, statistical power, subconscious reactions, summative evaluation, Student's t-test, the job of statistics, theoretical distribution, traditional statistics, typical user, uncontrolled factors, unrepeatable event, user experience design, user interface development, user studies, user test, validation, variability