Bachelor Statistics Lecture

Overview of the lecture structure

Day 1 - Why statistics matters

The Power of Statistics
Statistics as a part of science
A very short history of statistics
Key concepts of statistics

Day 2 - Data formats and descriptive stats

Continuous data
Data constructs and indices
Descriptive statistics

Day 3 - Data distribution and Probability

Normal distribution
Other distributions
Probability

Day 4 - Simple tests

Hypothesis testing
Confidence and Uncertainty
Ranks and other messy problems

Day 5 - Correlation

Significance again, residuals and sum of squares
Reliability and validity
Linear trends and transformations

Reading correlation plots

Day 6 - Regression

Causality
Assumptions & Diagnostics

Interpolation and extrapolation
XX

Day 7 - Design 1 - Simple Anova OR the lab experiment

The laboratory experiment

Designing experiments for testing a hypothesis
How to design a study
Explained and unexplained variance

Day 8 - Design 2 - Field experiments

Interaction effects
Replicates
Random factors

Day 9 - Case studies and natural experiments

Number of samples vs. number of variables
Transferability of single cases
Meta-Analysis

The map is not the territory

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?