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Hypothesis Testing in Python

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

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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.
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In the following Tracks

Certification Available

Data Analyst with Python

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Certification Available

Associate Data Scientist in Python

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Statistics Fundamentals with Python

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  1. 1

    Hypothesis Testing Fundamentals

    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.

    Play Chapter Now
    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. 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.

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For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Certification Available

Data Analyst with Python

Go To Track
Certification Available

Associate Data Scientist in Python

Go To Track

Statistics Fundamentals with Python

Go To Track

Datasets

Late ShipmentsStack OverflowU.S. Democrat Votes 2012/2016U.S. Republican Votes 2008/2012

Collaborators

Collaborator's avatar
Dr. Chester Ismay
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Amy Peterson
Collaborator's avatar
Izzy Weber

Prerequisites

Sampling in Python
James Chapman HeadshotJames 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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