10.1  Empirical statistics

10.1.1  Potential problems

10.1.2  How many?

10.2  Simulation statistics – bespoke tests

10.2.1  Do the maths (or try to)

10.2.2  Let the computer do the work

10.2.3  How many?

10.2.4  Estimating parameters

10.3  Synthetic data

10.3.1  Known model

10.3.2  Unknown model

ANOVA (Analysis of Variance), artificial intelligence, balanced experiment, Bayes rule, Bayesian statistics, bias, bimodal distribution, Binomial distribution, bootstrapping, categorical data, chatbot, closed formula, coin tossing, combinations, computer simulation, continuous data, correlation, cross-attribute frequencies, debias data, discrete data, distribution!parameters, domain expert, empirical data, empirical methods, empirical statistics, estimate, extreme statistics, extreme values, game engine, gpu, independence, independent samples, interquartile range, iterative computational algorithms, large language model, likelihood, Mann-Whitney test, mathematics, matrix algebra, maximum likelihood estimator, mean (μ), mechanism, median, nonparametric statistics, Normal distribution, observed frequencies, order statistics, p-value, parametric statistics, patient journey, Pearson correlation coefficient, permutation statistics, permuted values, permuting values, perturbation methods, perturbing individual values, Poisson distribution, posterior distribution, power-law distribution, prior distribution, probability distribution, random values, rare diseases, sampling bias, simulation methods, social network data, standard deviation (s.d., σ), statistical power, stochastic, stochastic simulation, Student's t-test, subsampling, synthetic data, theoretical distribution, traditional statistics, variance