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Hypothesis Testing in Python

4+
41 reviews
Intermediate

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 Exercises16,954 Learners

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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.

    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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In the following tracks

Data Analyst with PythonData Scientist with PythonData Scientist Professional with PythonStatistics Fundamentals with Python

Collaborators

Dr. Chester Ismay
Amy Peterson
Izzy Weber

Prerequisites

Sampling in Python
James Chapman HeadshotJames Chapman

Curriculum Manager, DataCamp

James is a Curriculum Manager 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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  • Muhanad A.
    2 days

    it gave a since to some previous courses like hypothesis testing and all the info about statics. anyhow, it'd be good to further clarify the purpose and how to invest in these tools.

  • Peter O.
    7 days

    Great content and the speaker was quite knowledgeable

  • Anisa B.
    28 days

    Very necessary this topic and it was very thoroughly constructed! I enjoyed working on it and getting to learn so many testing ways and where to use each of them.

  • abhishek d.
    30 days

    Explains statistics behind hypothesis testing effectively. Data professionals do not find this topic interesting. Course makes it interesting to grasp the concepts with practice examples. I recommend this course to everyone along with some conceptual reading on inferential statistics

  • Tomas H.
    about 1 month

    Great summary of the fundamental statistical concept.

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"it gave a since to some previous courses like hypothesis testing and all the info about statics. anyhow, it'd be good to further clarify the purpose and how to invest in these tools."

Muhanad A.

"Great content and the speaker was quite knowledgeable"

Peter O.

"Very necessary this topic and it was very thoroughly constructed! I enjoyed working on it and getting to learn so many testing ways and where to use each of them."

Anisa B.

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