Difference between revisions of "Bachelor Statistics Lecture"

From Sustainability Methods
Line 2: Line 2:
  
 
=== Day 1 - [[Why_statistics_matters|Why statistics matters]] ===
 
=== Day 1 - [[Why_statistics_matters|Why statistics matters]] ===
 +
<br>
 +
[[Why_statistics_matters#The_Power_of_Statistics|The Power of Statistics]]
 
<br>
 
<br>
 
[[Why_statistics_matters#Statistics_as_a_part_of_science|Statistics as a part of science]]
 
[[Why_statistics_matters#Statistics_as_a_part_of_science|Statistics as a part of science]]
Line 8: Line 10:
 
<br>
 
<br>
 
[[Why_statistics_matters#Key_concepts_of_statistics|Key concepts of statistics]]
 
[[Why_statistics_matters#Key_concepts_of_statistics|Key concepts of statistics]]
<br>
 
[[The Power of Statistics|The Power of Statistics]]
 
  
 
=== Day 2 - [[Data formats]] and descriptive stats ===
 
=== Day 2 - [[Data formats]] and descriptive stats ===

Revision as of 11:30, 6 April 2020

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 - Distribution


Normal distribution
Other distributions
Probabilities

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?