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Hypothesis Testing in R

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

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4 Hours16 Videos53 Exercises5,161 Learners
4000 XP

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Course Description

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 side-step the requirements of traditional hypothesis tests. Throughout the course, you'll explore a Stack Overflow user survey, and a dataset of late shipments of medical supplies.

  1. 1

    Yum, That Dish Tests Good


    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
    To the lab for testing
    50 xp
    Uses of A/B testing
    50 xp
    Calculating the sample mean
    100 xp
    Calculating a z-score
    100 xp
    A tail of two z's
    50 xp
    Criminal trials and hypothesis tests
    50 xp
    Left tail, right tail, two tails
    100 xp
    Calculating p-values
    100 xp
    Statistically significant other
    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

StatisticianStatistics Fundamentals


Dr. Chester Ismay


Sampling in R
Richie Cotton Headshot

Richie Cotton

Curriculum Architect at DataCamp

Richie is a Learning Solutions Architect 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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