When Predictions Fail S01E43
Today we’re going to talk about why many predictions fail - specifically we’ll take a look at the 2008 financial crisis, the 2016 U.S. presidential election, and earthquake prediction in general. From inaccurate or just too little data to biased models and polling errors, knowing when and why we make inaccurate predictions can help us make better ones in the future. And even knowing what we can’t predict can help us make better decisions too.
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1
What Is Statistics
2
Mathematical Thinking
3
Mean, Median, and Mode: Measures of Central Tendency
4
Measures of Spread
5
Charts Are Like Pasta - Data Visualization Part 1
6
Plots, Outliers, and Justin Timberlake: Data Visualization Part 2
7
The Shape of Data: Distributions
8
Correlation Doesn’t Equal Causation
9
Controlled Experiments
10
Sampling Methods and Bias with Surveys
11
Science Journalism
12
Henrietta Lacks, the Tuskegee Experiment, and Ethical Data Collection
13
Probability Part 1: Rules and Patterns
14
Probability Part 2: Updating Your Beliefs with Bayes
15
The Binomial Distribution
16
Geometric Distributions and The Birthday Paradox
17
Randomness
18
Z-Scores and Percentiles
19
The Normal Distribution
20
Confidence Intervals
21
How P-Values Help Us Test Hypotheses
22
P-Value Problems
23
Playing with Power: P-Values Pt 3
24
You Know I’m All About that Bayes
25
Bayes in Science and Everyday Life
26
Test Statistics
27
T-Tests: A Matched Pair Made in Heaven
28
Degrees of Freedom and Effect Sizes
29
Chi-Square Tests
30
P-Hacking
31
The Replication Crisis
32
Regression
33
ANOVA
34
ANOVA Part 2: Dealing with Intersectional Groups
35
Fitting Models Is like Tetris
36
Supervised Machine Learning
37
Unsupervised Machine Learning
38
Intro to Big Data
39
Big Data Problems
40
Statistics in the Courts
41
Neural Networks
42
War
43
When Predictions Fail
44
When Predictions Succeed
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