The text gives a concise introduction into fundamental concepts in
statistics. Chapter 1: Short exposition of probability theory, using
generic examples. Chapter 2: Estimation in theory and practice, using
biologically motivated examples. Maximum-likelihood estimation in
covered, including Fisher information and power computations. Methods
for calculating confidence intervals and robust alternatives to standard
estimators are given. Chapter 3: Hypothesis testing with emphasis on
concepts, particularly type-I, type-II errors, and interpreting test
results. Several examples are provided. T-tests are used throughout,
followed important other tests and robust/nonparametric alternatives.
Multiple testing is discussed in more depth, and combination of
independent tests is explained. Chapter 4: Linear regression, with
computations solely based on R. Multiple group comparisons with ANOVA
are covered together with linear contrasts, again using R for
computations.