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 21 How P-Values Help Us Test Hypotheses
2.00 13 minutes Today we're going to begin our three-part unit on p-values. In this episode we'll talk about Null Hypothesis Significance Testing (or NHST) which is a framework for comparing two sets of information. In NHST we assume that there is no difference between the two things we are observing and and use our p-value as a predetermined cutoff for if something seems sufficiently rare or not to allow us to reject that these two observations are the same. This p-value tells us if something is statistically significant, but as you'll see that doesn't necessarily mean the information is significant or meaningful to you.
