At the end of this course the participant

- has insight in the√n law and its consequences for sample size
- has insight in the general principles of decision procedures (“testing”), and is able to apply these procedures in practice using common statistical packages (SPSS, R)
- understands the principles of the following statistical analysis techniques:
o Student T tests (1-sample, 2-sample and paired)
o Analysis of Variance (1-way and 2-way ANOVA)
o Simple and multiple linear regression analysis
o 1-sample, 2-sample and paired proportion tests (χ 2 test for goodness-of-fit, Pearson’s χ 2 test and McNemar’s χ 2 test)
- knows in which situations these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
- is able to apply these techniques using common statistical packages (SPSS, R)
- has insight in the Kolmogorov Smirnov test (normal distribution) and the Fisher test for equality of variances and is able to apply these tests in practice using common statistical packages (SPSS, R)
- understands the results obtained with these techniques, and is able to apply these results in practice (e.g. in answering a study question)
- is familiar with the terms ‘explained variance’ and multi-collinearity.
- understands the principles of model reduction in regression analysis
- understands the basic principles of the technique of logistic regression analysis
- is able to choose the appropriate non-parametric technique to be applied in case of non-normally distributed data, and understands the principles of these methods.
- is able to apply these techniques using common statistical packages (SPSS, R)
- understands the results obtained with these techniques, and is able to apply these results in practice (e.g. in answering a study question)