# Hypothesis Testing in Python

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

4 Hours15 Videos50 Exercises6,560 Learners3750 XPData Analyst TrackData Scientist TrackStatistics Fundamentals Track

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## Course Description

Hypothesis testing lets you answer questions about your datasets in a statistically rigorous way. In this course, you'll grow your Python analytical skills as you learn how and when to use common tests like t-tests, proportion tests, and chi-square tests. Working with real-world data, including Stack Overflow user feedback, you'll gain a deep understanding of how these tests work, and the key assumptions that underpin them. You'll also discover how different tests are related using the “there is only one test" framework, before learning how to use non-parametric tests to go beyond the limitations of side-step the requirements of hypothesis tests.

1. 1

### Introduction to Hypothesis Testing

Free

How does hypothesis testing work and what problems can it solve? To find out, you’ll walk through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-values, and false negative and false positive errors.

Hypothesis tests and z-scores
50 xp
Uses of A/B testing
50 xp
Calculating the sample mean
100 xp
Calculating a z-score
100 xp
p-values
50 xp
Criminal trials and hypothesis tests
50 xp
Left tail, right tail, two tails
100 xp
Calculating p-values
100 xp
Statistical significance
50 xp
Decisions from p-values
50 xp
Calculating a confidence interval
100 xp
Type I and type II errors
100 xp
2. 2

### Two-Sample and ANOVA Tests

In this chapter, you’ll learn how to test for differences in means between two groups using t-tests and extend this to more than two groups using ANOVA and pairwise t-tests.

3. 3

### Proportion Tests

Now it’s time to test for differences in proportions between two groups using proportion tests. Through hands-on exercises, you’ll extend your proportion tests to more than two groups with chi-square independence tests, and return to the one sample case with chi-square goodness of fit tests.

4. 4

### Non-Parametric Tests

Finally, it’s time to learn about the assumptions made by parametric hypothesis tests, and see how non-parametric tests can be used when those assumptions aren't met.

In the following tracks

Data Analyst Data Scientist Statistics Fundamentals

Collaborators

Prerequisites

Sampling in Python #### James Chapman

Content Developer, DataCamp

James is a Content Developer at DataCamp. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in quasar detection and tutored Math and English. He joined DataCamp as a learner in 2018, and the data skills learned on DataCamp were quickly integrated into his scientific projects. In his spare time, he enjoys restoring retro toys and electronics.