Bachelor Statistics Lecture

Day 1 - Why statistics?
A very short history of statistics.
The Power of statistics
The scientific landscape and statistics

Day 2 - Data formats and descriptive stats Continuous data Data constructs and indices Descriptive statistics

Day 3 - Distribution Normal distribution Other distributions Probabilities

Day 4 - Simple tests Normal distribution Other distributions Probability

Day 5 - Correlation Significance, residuals and sum of squares Reliability and validity Transformations

Day 6 - Regression Causality Prediction XX

Day 7 - Design 1 - Simple Anaya OR the lab experiment Designing an experiment Controlled variables Explained variance

Day 8 - Design 2 - Field experiments Interaction effects Replicates Random factors

Day 9 - Case studies and natural experiments Number of variables vs number of samples Transferability of single cases Meta-Analysis

Day 10 - Bias Bias associated to sampling Bias within analysis Bias related to interpretation of data and analysis

Day 11 - Limits of statistics Mixed methods Statistics and disciplines A qualitative methods view on statistics

Day 12 - A word on ethics Confusion through statistics Statistics serving immoral goals How statistics can fuel anger

Day 13 - The Big recap What did we learn? Why does it matter? How to go on?