Season
Season 1 Episode
- Season 1 What Is Statistics
- Season 1 Mathematical Thinking
- Season 1 Mean, Median, and Mode: Measures of Central Tendency
- Season 1 Measures of Spread
- Season 1 Charts Are Like Pasta - Data Visualization Part 1
- Season 1 Plots, Outliers, and Justin Timberlake: Data Visualization Part 2
- Season 1 The Shape of Data: Distributions
- Season 1 Correlation Doesn’t Equal Causation
- Season 1 Controlled Experiments
- Season 1 Sampling Methods and Bias with Surveys
- Season 1 Science Journalism
- Season 1 Henrietta Lacks, the Tuskegee Experiment, and Ethical Data Collection
- Season 1 Probability Part 1: Rules and Patterns
- Season 1 Probability Part 2: Updating Your Beliefs with Bayes
- Season 1 The Binomial Distribution
- Season 1 Geometric Distributions and The Birthday Paradox
- Season 1 Randomness
- Season 1 Z-Scores and Percentiles
- Season 1 The Normal Distribution
- Season 1 Confidence Intervals
- Season 1 How P-Values Help Us Test Hypotheses
- Season 1 P-Value Problems
- Season 1 Playing with Power: P-Values Pt 3
- Season 1 You Know I’m All About that Bayes
- Season 1 Bayes in Science and Everyday Life
- Season 1 Test Statistics
- Season 1 T-Tests: A Matched Pair Made in Heaven
- Season 1 Degrees of Freedom and Effect Sizes
- Season 1 Chi-Square Tests
- Season 1 P-Hacking
- Season 1 The Replication Crisis
- Season 1 Regression
- Season 1 ANOVA
- Season 1 ANOVA Part 2: Dealing with Intersectional Groups
- Season 1 Fitting Models Is like Tetris
- Season 1 Supervised Machine Learning
- Season 1 Unsupervised Machine Learning
- Season 1 Intro to Big Data
- Season 1 Big Data Problems
- Season 1 Statistics in the Courts
- Season 1 Neural Networks
- Season 1 War
- Season 1 When Predictions Fail
- Season 1 When Predictions Succeed
Crash Course Statistics - Season 1 Episode 43 When Predictions Fail
2.00 13 minutes 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.
