4.1  Types of probability distribution

4.1.1  Continuous or discrete?

4.1.2  Finite or unbounded

4.1.3  UK income distribution – a long tail

4.1.4  One tail or two?

4.2  Normal or not?

4.2.1  Approximations

4.2.2  The central limit theorem – (nearly) everything is Normal

4.2.3  Non-Normal – what can go wrong?

4.2.4  Power law distributions

4.2.5  Parametric and Nonparametric

ANOVA (Analysis of Variance), applied mathematics, approximate distribution, approximately Normal, arithmetical data, asymmetric distribution, bias, bimodal distribution, binary data, Binomial distribution, bounded below, bounded data, categorical data, Central Limit Theorem, citation data, coin tossing, continuous data, continuous distribution, count data, discrete data, empirical distribution, error rate, fair coin, false negative, feedback effects, finite data, finite variance, income distribution, independence condition, independent, larger samples, Likert scale, linear regression, linearity condition, Log-Normal distribution, long-tail distribution, mathematics, mean (μ), negative binomial, network phenomena, nominal data, non-independence, nonlinearity, nonparametric statistics, Normal distribution, Normal distribution, one-tailed test, ordinal data, parametric statistics, Poisson distribution, positive feedback, power (statistical), power-law distribution, probability distribution, quartile, residuals, sample size, scale free distribution, social network data, standard deviation (s.d., σ), statistical analysis, statistical power, Student's t-test, survey data, Student's t-test, tail, tail heavy, task completion time, theoretical distribution, threshold effect, tossing coins, transforming data, two-tailed test, UK income distribution, unbounded above, unbounded data, unbounded tail, unbounded values, user experience studies, user interface testing, user test, variability, variance