Basic Statistics Learning Outcomes

Subject specific

• Familiarity with the concept of data (samples & populations, different types of variables, accuracy)
• Knowledge of mathematical basics
• Basic knowledge of classic probability theory (including Bayes’ theorem and its scientific implications as well as an overview about the different concepts of probability); basic knowledge of the most common probability distributions
• Knowledge of and the ability to interpret univariate and multivariate standard descriptive statistics
• Basic theoretical understanding and the ability to critically interpret statistical inference and hypothesis testing (including randomization tests)
• Ability to critically assess causalities from correlations and (multiple) regressions; basic knowledge and practical ability to represent causal relationships (path models)
• Multivariate ordination techniques (including principal components and factor analysis)
• Relating several variables: multivariate regression, canonical correlation analysis and partial least squares

Methodological

• Ability to plan and carry out typical statistical analyses and to critically assess statistical outcomes
• Statistical methods as quantitative reasoning in science (including graphical presentation of data and statistical results)
• Ability to carry out relevant statistical procedures using Mathematica, Matlab, R, SPSS, or other statistics programmes

Generic

Systemic

• quantitative and probabilistic thinking

Go back to Methods Modules.