It is strongly recommended that students have successfully completed mathematics at year 12 level.

This subject introduces students to biostatistics as applied to Public Health. It delivers the basic technical qualifications necessary for analysing data on a descriptive and bivariate level.

Introduction to probability. The basic terminology of the theory of probability, such as probability itself, conditional probability and independent events will be discussed.

Diagnostic tests. Sensitivity and specificity of a diagnostic test and the relationship with prevalence, positive predictive value and negative predictive value are detailed. Bayes' theorem and ROC-curves are introduced.

Theoretical and empirical distributions. The most important theoretical distributions in medical statistics, like normal distribution, binomial distribution and poisson distribution will be introduced and exercised.

Measures of central tendency and dispersion. Students will be familiarised with mean, median, mode, percentiles, standard deviation, variance and range. Use and misuse of these descriptive statistics are discussed.

Graphical display. The most important means of graphical display are introduced, including histogram, stem-and-leaf plot, bar chart, scattergram and box-and-whisker plot.

Confidence interval. The basic principles of statistical inference are detailed. Confidence intervals are introduced for mean, median, proportion, difference of means and difference of proportions.

The principle of statistical testing. Statistical hypothesis testing as the core mean of statistical inference is introduced in general. The understanding of sample, population, repeated sampling, central limit theorem, critical value, test statistic, alpha, beta, Null Hypothesis and Alternative Hypothesis, one-sided and two-sided tests, paired and unpaired tests and p-value will be explained in detail. The relationship to sample size determination and the problem of multiple testing is discussed. Sample size determination will be exercised.

Bivariate statistical tests. Common bivariate statistical tests and their proper application are detailed. Test procedures covered include paired and unpaired t-test, paired and unpaired Wilcoxon tests, Kruskal-Wallis test, Friedman test, analysis of variance, regression and correlation, common chi-square test, McNemar test, chi-square test for trend. Intensive use of examples and exercises will help to enhance the practical understanding of these techniques.

Survival analysis. The actuarial and the product-limit procedures to estimate the cumulative survival probability are discussed. Methods of comparison of cumulative survival probabilities, like logrank test, are introduced.