Students who have completed this course - can explain and provide example for what the different steps in scientific inference are. - know the differences among the main types of study design (observational vs experimental, randomization, double-blind etc.). - know the importance of random vs convenience sampling, and how to stratify sampling. - know the critical assumptions of statistical models such as linear and generalized models, specifically independence and the mean-variance relationship. - know how to interpret parameters estimated using statistical models, and how to interpret and deal with uncertainty. Students who have completed this course - can design experimental studies to investigate main effects and their interactions. - can design observational studies, particularly with regard to confounding. - can decide on which statistical models should be used based on assumptions and data characteristics. - know how to use generalized linear models (linear regression, ANOVA, ANCOVA, logistic regression, log-linear models) and how to interpret parameter estimates and their uncertainty. - can organize and analyze data sets using R. Students who have completed this course - are aware of the importance of all steps in the processes of scientific inference, from formulating the biological question, to designing the study, analyzing the data and interpreting the results statistical analysis. - know the main reasons for choosing different types of studies (experimental observational) and designs. - know the importance of assumptions when using statistical models for the robustness of the conclusions, and the relative importance of assumptions (independence, variance-mean relationship, normality, etc.). - know how to focus on the biological significance and interpretation of parameters rather than statistical significance. - know how to make research reproducible through the use of scripts with detailed documentation.