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Playing on server:
- Episode 1 What Is Statistics
- Episode 2 Mathematical Thinking
- Episode 3 Mean, Median, and Mode: Measures of Central Tendency
- Episode 4 Measures of Spread
- Episode 5 Charts Are Like Pasta - Data Visualization Part 1
- Episode 6 Plots, Outliers, and Justin Timberlake: Data Visualization Part 2
- Episode 7 The Shape of Data: Distributions
- Episode 8 Correlation Doesn’t Equal Causation
- Episode 9 Controlled Experiments
- Episode 10 Sampling Methods and Bias with Surveys
- Episode 11 Science Journalism
- Episode 12 Henrietta Lacks, the Tuskegee Experiment, and Ethical Data Collection
- Episode 13 Probability Part 1: Rules and Patterns
- Episode 14 Probability Part 2: Updating Your Beliefs with Bayes
- Episode 15 The Binomial Distribution
- Episode 16 Geometric Distributions and The Birthday Paradox
- Episode 17 Randomness
- Episode 18 Z-Scores and Percentiles
- Episode 19 The Normal Distribution
- Episode 20 Confidence Intervals
- Episode 21 How P-Values Help Us Test Hypotheses
- Episode 22 P-Value Problems
- Episode 23 Playing with Power: P-Values Pt 3
- Episode 24 You Know I’m All About that Bayes
- Episode 25 Bayes in Science and Everyday Life
- Episode 26 Test Statistics
- Episode 27 T-Tests: A Matched Pair Made in Heaven
- Episode 28 Degrees of Freedom and Effect Sizes
- Episode 29 Chi-Square Tests
- Episode 30 P-Hacking
- Episode 31 The Replication Crisis
- Episode 32 Regression
- Episode 33 ANOVA
- Episode 34 ANOVA Part 2: Dealing with Intersectional Groups
- Episode 35 Fitting Models Is like Tetris
- Episode 36 Supervised Machine Learning
- Episode 37 Unsupervised Machine Learning
- Episode 38 Intro to Big Data
- Episode 39 Big Data Problems
- Episode 40 Statistics in the Courts
- Episode 41 Neural Networks
- Episode 42 War
- Episode 43 When Predictions Fail
- Episode 44 When Predictions Succeed
1 Views
Crash Course Statistics - Season 1 Episode 23 Playing with Power: P-Values Pt 3
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We're going to finish up our discussion of p-values by taking a closer look at how they can get it wrong, and what we can do to minimize those errors. We'll discuss Type 1 (when we think we've detected an effect, but there actually isn't one) and Type 2 (when there was an effect we didn't see) errors and introduce statistical power - which tells us the chance of detecting an effect if there is one.
Runtime: 13:14 minutes
Quality: HD
First Air Date: Jan 24, 2018
Last Air date: Jan 09, 2019
Episode: 44 Episode
Season: 1 Season
IMDb: 9
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