4.2 Hypothesis Testing
Expand your statistics toolkit, learning how to make good decisions with limited data.
Hypothesis Testing
The Z-statistic
The Z-test
Understanding p-values
p-hacking
Power
Practice: Power & Error
Confidence Intervals
The Chi-Square Statistic I
The Chi-Square Statistic II
Chi-Square Random Variables
Degrees of Freedom I
Point Estimates
Degrees of Freedom II
Homogeneity Tests
Independence Tests
A Tale of Two Cities' Proportions
Intro to t-variables
Pooled Variance
Unpooled Variance
Why ANOVA?
Linear Regression: The Simplest Model
Best-Fit Lines
The Linear Regression F-statistic
Linear Regression ANOVA Tables
ANOVA and Mean Comparisons
Course description
Have you ever wanted to use data to test a hypothesis, prove a point, or even just make meaning of the world? Statistics is essential for achieving all of those goals, and this course will teach you the methods you need to make the most of your data. You'll gain hands-on experience designing experiments and framing questions for statistical analysis. You'll also expand your statistics toolkit to include a suite of powerful hypothesis tests.
Topics covered
- ANOVA
- Chi-square test
- Contingency tables
- F-test
- Goodness of fit
- Power
- p-value
- t-test
Prerequisites and next steps
The basics of statistics covered in a first semester stats course is crucial. Familiarity with some basic concepts from probability, such as distribution functions, mean, variance, and the central limit theorem, is necessary. Some calculus knowledge will be helpful, but not essential.
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Delve into the inner machinery of neural networks to discover how these flexible learning tools actually work.
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