Bachelor Statistics Lecture

Overview of the lecture structure

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 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 ignorance

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