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.
Focus on concepts rather than long calculations
Several topics relevant to modern biology (multiple testing, minimax estimation) usually not found in similar texts
Concise first introduction or repetition of fundamental concepts in statistics - Pointers to relevant R functions and datatypes
Robust and nonparametric counterparts of classical estimators and tests given throughout the text
Basics of Probability Theory.- Estimation.- Hypothesis Testing.- Regression.- References.- Index.