Difference between revisions of "Bachelor Statistics Lecture"
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The scientific landscape and statistics | The scientific landscape and statistics | ||
− | Day 2 - Data formats and descriptive stats | + | Day 2 - [[Data formats]] and descriptive stats |
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Continuous data | Continuous data | ||
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Descriptive statistics | Descriptive statistics | ||
− | + | [[Data distribution|Day 3 - Distribution]] | |
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[[Data_distribution#The_normal_distribution|Normal distribution]] | [[Data_distribution#The_normal_distribution|Normal distribution]] | ||
<br> | <br> | ||
− | Other distributions | + | [[Data distribution|Other distributions]] |
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Probabilities | Probabilities |
Revision as of 14:47, 7 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
Normal distribution
Other distributions
Probability
Day 5 - Correlation
Significance, residuals and sum of squares
Reliability and validity
Transformations
Day 6 - Regression
Causality
Prediction
XX
Day 7 - Design 1 - Simple Anaya OR the lab experiment
Designing an experiment
Controlled variables
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?