Difference between revisions of "Bachelor Statistics Lecture"

From Sustainability Methods
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[[Bias | Groundwork]]
 
[[Bias | Groundwork]]
 
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[[https://sustainabilitymethods.org/index.php/Bias#Bias_in_analyzing_data | Different biases]]
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[[https://sustainabilitymethods.org/index.php/Bias#Bias_in_analyzing_data Different biases]]
 
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[[https://sustainabilitymethods.org/index.php/Bias#A_world_beyond_Bias.3F | How to deal with biases?]]
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[[https://sustainabilitymethods.org/index.php/Bias#A_world_beyond_Bias.3F How to deal with biases?]]
  
 
=== Day 11 - Limits of statistics ===
 
=== Day 11 - Limits of statistics ===

Revision as of 21:04, 8 June 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 - Data distribution and Probability


Normal distribution
Other distributions
Probability

Day 4 - Hypothesis building and simple tests


Hypothesis testing
Confidence, uncertainty and reliability
Simple tests - a primer

Day 5 - Correlations


Correlations on a shoestring
Reading correlation plots
Correlative relations

Day 6 - Regression


Causality
Residuals
Significance in regressions
Is the world linear?
Interpolation and extrapolation

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


The laboratory experiment
How do I compare more than two groups?
How to design a study

Day 8 - Design 2 - Field experiments

Day 9 - Case studies and natural experiments

Day 10 - Bias


Groundwork
[Different biases]
[How to deal with biases?]

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?