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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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4 Hours15 Videos50 Exercises9,876 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 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. 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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In the following tracks

Data Analyst Data Scientist Statistics Fundamentals

Collaborators

chesterismay
Dr. Chester Ismay
amy-4121b590-cc52-442a-9779-03eb58089e08
Amy Peterson
izzyweber-9bc35945-95bd-423b-833e-40780c76586f
Izzy Weber

Prerequisites

Sampling in Python
James Chapman Headshot

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.

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