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.

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