# 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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## 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
### Yum, That Dish Tests Good

**Free**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.

To the lab for testing50 xpUses of A/B testing50 xpCalculating the sample mean100 xpCalculating a z-score100 xpA tail of two z's50 xpCriminal trials and hypothesis tests50 xpLeft tail, right tail, two tails100 xpCalculating p-values100 xpStatistically significant other50 xpDecisions from p-values50 xpCalculating confidence intervals100 xpType I and type II errors100 xp - 2
### Pass Me ANOVA Glass of Iced t

Learn how to test for differences in means between two groups using t-tests, and how to extend this to more than two groups using ANOVA and pairwise t-tests.

Is this some kind of test statistic?50 xpTwo sample mean test statistic100 xpHypothesis testing workflow100 xpTime for t50 xpWhy is t needed?50 xpThe t-distribution50 xpFrom t to p100 xpPairing is caring50 xpIs pairing needed?100 xpVisualizing the difference100 xpUsing t.test()100 xpP-hacked to pieces50 xpVisualizing many categories100 xpANOVA100 xpPairwise t-tests100 xp - 3
### Letting the Categoricals Out of the Bag

Learn how to test for differences in proportions between two groups using proportion tests, extended it to more than two groups with chi-square independence tests, and return to the one sample case with chi-square goodness of fit tests.

Difference strokes for proportions, folks50 xpt for proportions?50 xpTest for single proportions100 xpA sense of proportion50 xpTest for two proportions100 xpprop_test() for two samples100 xpDeclaration of independence50 xpThe chi-square distribution50 xpHow many tails for chi-square tests?50 xpChi-square test of independence100 xpDoes this dress make my fit look good?50 xpVisualizing goodness of fit100 xpChi-square test of goodness of fit100 xp - 4
### Time to Define the Relationship

Learn about the assumptions made by parametric hypothesis tests, and saw how simulation-based and rank-based non-parametric tests can be used when those assumptions aren't met.

What do you assume?50 xpCommon assumptions of hypothesis tests50 xpTesting sample size100 xpX-ray specs: don't believe the hyp50 xpThere is only one test50 xpSpecifying & hypothesizing100 xpThe generation game50 xpGenerating & calculating100 xpObserved statistic and p-value100 xpLook ma! No parameters!50 xpSimulation-based t-test100 xpRank sum tests100 xpCongratulations50 xp

Datasets

Late shipmentsLate shipments bootstrap distributionDemocratic presidential candidates by countyCollaborators

Dr. Chester IsmayPrerequisites

Sampling in R#### 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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Lloyds Banking Group

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Harvard Business School

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Ronald Bowers

Decision Science Analytics, USAA

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