Difference between revisions of "Bachelor Statistics Lecture"
| Line 9: | Line 9: | ||
The scientific landscape and statistics | The scientific landscape and statistics | ||
| − | Day 2 - [[Data formats]] and descriptive stats | + | === Day 2 - [[Data formats]] and descriptive stats === |
<br> | <br> | ||
Continuous data | Continuous data | ||
| Line 17: | Line 17: | ||
Descriptive statistics | Descriptive statistics | ||
| − | + | === Data distribution|Day 3 - Distribution === | |
<br> | <br> | ||
[[Data_distribution#The_normal_distribution|Normal distribution]] | [[Data_distribution#The_normal_distribution|Normal distribution]] | ||
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Probabilities | Probabilities | ||
| − | Day 4 - Simple tests | + | === Day 4 - Simple tests === |
<br> | <br> | ||
Normal distribution | Normal distribution | ||
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Probability | Probability | ||
| − | Day 5 - Correlation | + | === Day 5 - Correlation === |
<br> | <br> | ||
Significance, residuals and sum of squares | Significance, residuals and sum of squares | ||
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Transformations | Transformations | ||
| − | Day 6 - Regression | + | === Day 6 - Regression === |
<br> | <br> | ||
Causality | Causality | ||
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XX | XX | ||
| − | Day 7 - Design 1 - Simple Anaya OR the lab experiment | + | === Day 7 - Design 1 - Simple Anaya OR the lab experiment === |
<br> | <br> | ||
Designing an experiment | Designing an experiment | ||
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Explained variance | Explained variance | ||
| − | Day 8 - Design 2 - Field experiments | + | === Day 8 - Design 2 - Field experiments === |
<br> | <br> | ||
Interaction effects | Interaction effects | ||
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Random factors | Random factors | ||
| − | Day 9 - Case studies and natural experiments | + | === Day 9 - Case studies and natural experiments === |
<br> | <br> | ||
Number of variables vs number of samples | Number of variables vs number of samples | ||
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Meta-Analysis | Meta-Analysis | ||
| − | Day 10 - Bias | + | === Day 10 - Bias === |
<br> | <br> | ||
Bias associated to sampling | Bias associated to sampling | ||
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Bias related to interpretation of data and analysis | Bias related to interpretation of data and analysis | ||
| − | Day 11 - Limits of statistics | + | === Day 11 - Limits of statistics === |
<br> | <br> | ||
Mixed methods | Mixed methods | ||
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A qualitative methods view on statistics | A qualitative methods view on statistics | ||
| − | Day 12 - A word on ethics | + | === Day 12 - A word on ethics === |
<br> | <br> | ||
Confusion through statistics | Confusion through statistics | ||
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How statistics can fuel ignorance | How statistics can fuel ignorance | ||
| − | Day 13 - The Big recap | + | === Day 13 - The Big recap === |
<br> | <br> | ||
What did we learn? | What did we learn? | ||
Revision as of 11:36, 9 March 2020
Contents
-
1 Overview of the lecture structure
- 1.1 Day 1 - Why statistics?
- 1.2 Day 2 - Data formats and descriptive stats
- 1.3 Data distribution|Day 3 - Distribution
- 1.4 Day 4 - Simple tests
- 1.5 Day 5 - Correlation
- 1.6 Day 6 - Regression
- 1.7 Day 7 - Design 1 - Simple Anaya OR the lab experiment
- 1.8 Day 8 - Design 2 - Field experiments
- 1.9 Day 9 - Case studies and natural experiments
- 1.10 Day 10 - Bias
- 1.11 Day 11 - Limits of statistics
- 1.12 Day 12 - A word on ethics
- 1.13 Day 13 - The Big recap
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
Data distribution|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?
