# Hypothesis Testing in Python

4.0+
63 reviews
Intermediate

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

4 Hours15 Videos50 Exercises

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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 and supply-chain data for medical supply shipments, you'll gain a deep understanding of how these tests work and the key assumptions that underpin them. You'll also discover how non-parametric tests can be used to go beyond the limitations of traditional 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.

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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 with PythonData Scientist with PythonData Scientist Professional with PythonStatistics Fundamentals with Python

Collaborators

Prerequisites

Sampling in Python
James Chapman

Curriculum Manager, DataCamp

James is a Curriculum Manager at DataCamp, where he collaborates with experts from industry and academia to create courses on AI, data science, and analytics. He has led nine DataCamp courses on diverse topics in Python, R, AI developer tooling, and Google Sheets. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in high-redshift quasar detection. In his spare time, he enjoys restoring retro toys and electronics.

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## Don’t just take our word for it

*4.0
from 63 reviews
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• Ruben N.
18 days

Good material, very professional explanations and the subject is totally applicable in real life work.

• Alan S.
18 days

The course was very well designed and clearly explained. The examples were very helpful to fully understand the subject.

• Mark K.
21 days

The course is well structured and goes into the appropriate depth required for a data scientist. Lots of practice exercises and projects.

• Jose R.

great course

• shankar M.

Amazing course with detailed coverage of elementary statistics using Python. Library pingouin excited to use python on testing of hypothesis. Content is quiet good and simplified to understand for any commoners.

"Good material, very professional explanations and the subject is totally applicable in real life work."

Ruben N.

"The course was very well designed and clearly explained. The examples were very helpful to fully understand the subject."

Alan S.

"The course is well structured and goes into the appropriate depth required for a data scientist. Lots of practice exercises and projects."

Mark K.