How do I know what the most appropriate statistical test for my data is?
What is multiple testing and why does it matter?
What should a histogram of p values look like, if H0 was true?
How do I measure the exact content of information in my data and how can I unveil information that lays dormant in the data without over interpreting it?
We are going to answer all these questions with hands-on examples by teaching not only the classical methods, but also more suitable tools like Bayesian Statistics.
- basic probability density functions and their relations
- mean, variance and covariance
- t-test, z-test, ANOVA, MANOVA
- non-parametric tests
- multiple testing
- analyzing count data (peptides, DESeq)
- principal component analysis
- bayesian statisitcs