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  Questions for statistics – what do you want to know?

1.6  What next

A–B test, artificial intelligence, base rate, Bayesian reasoning, Bayesian statistics, bias, big data, Binomial distribution, chatgpt, cherry picking, coin tossing, confidence interval, conscious thinking, descriptive statistics, effect size, empirical data, empirical methods, empirical statistics, epistemologically random, error bars, error rate, estimate, expert evaluation, explanation, exploration, 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, large language model, linear regression, machine learning, mathematics, mean (μ), mechanism, Normal distribution, odds ratio, ontologically random, p-value, plausibility, population, probabilistic phenomena, probability distribution, qualitative methods, R, random effect, random number generators, real world, sample, Schrodinger's cat, seeded, significance test, simulation methods, software development process, standard deviation (s.d., σ), statistical analysis, statistical power, Student's t-test, subconscious reactions, summative evaluation, the job of statistics, theoretical distribution, traditional statistics, typical user, uncertainty measure, uncontrolled factors, unrepeatable event, user experience design, user interface development, user studies, validation, variability, visualisation