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Hypothesis Testing in R

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4 hours
4,000 XP
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Course Description

Discover Hypothesis Testing in R

Hypothesis testing lets you ask questions about your datasets and answer them in a statistically rigorous way. In this course, you'll learn how and when to use common tests like t-tests, proportion tests, and chi-square tests.

You'll gain a deep understanding of how they work and the assumptions that underlie them. You'll also learn how different hypothesis tests are related using the ""There is only one test"" framework and use non-parametric tests that let you sidestep the requirements of traditional hypothesis tests.

Learn About T-Tests and Chi-Square Tests

You’ll start by learning why hypothesis testing in R is useful while examining some key concepts as you go. You’ll also learn how t-tests can help you test for differences in means between two groups and how chi-square tests can help you compare observed results with expected results.

Understand the Relationships Between R Hypothesis Tests

As you progress, you’ll discover the relationships between different tests, exploring elements of randomness, independence of observation, and sample sizes.

By the time you finish this course, you’ll have a deeper understanding of hypothesis testing in R and when it’s appropriate to use specific tests on your data.

Throughout the course, you'll explore a Stack Overflow user survey and a dataset of late shipments of medical supplies."
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  1. 1

    Introduction to Hypothesis Testing


    Learn why hypothesis testing is useful, and step through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-p-values, and false negative and false positive errors. The Stack Overflow survey and late medical shipments datasets are introduced.

    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
    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 confidence intervals
    100 xp
    Type I and type II errors
    100 xp

In the following tracks

Associate Data ScientistData Analyst StatisticianStatistics Fundamentals


Collaborator's avatar
Dr. Chester Ismay


Sampling in R
Richie Cotton HeadshotRichie Cotton

Data Evangelist at DataCamp

Richie is a Data Evangelist at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
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