Bachelor Statistics Lecture

Overview of the lecture structure

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

[Hypothesis building|[Hypothesis testing]]
Confidence
Rank

Day 5 - Correlation

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

Reading correlation plots

Day 6 - Regression

Causality
Prediction
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 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?