Difference between revisions of "Bachelor Statistics Lecture"

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[[Data_distribution#The Poisson distribution|Other distributions]]
 
[[Data_distribution#The Poisson distribution|Other distributions]]
 
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Probabilities
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[[A matter of probability|Probabilities]]
  
 
=== Day 4 - Simple tests ===
 
=== Day 4 - Simple tests ===

Revision as of 11:53, 10 March 2020

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 testing
Confidence and Uncertainty
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
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 variables vs number of samples
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?