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.

Hypothesis tests and z-scores

Uses of A/B testing

Calculating the sample mean

Calculating a z-score

p-values

Criminal trials and hypothesis tests

Options

Two tails

Left tail

Right tail

Left tail, right tail, two tails

Calculating p-values

Statistical significance

Decisions from p-values

Calculating confidence intervals

False positive (Type I) errors

False negative (Type II) errors

Not an error

Type I and type II errors

Introduction to Hypothesis Testing

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.

Performing t-tests

Hypothesis testing workflow

Two sample mean test statistic

Calculating p-values from t-statistics

Why is t needed?

The t-distribution

From t to p

Paired t-tests

Is pairing needed?

Visualizing the difference

Using t.test()

ANOVA tests

Visualizing many categories

Conducting an ANOVA test

Pairwise t-tests

Two-Sample and ANOVA Tests

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.

One-sample proportion tests

t for proportions?

Test for single proportions

Two-sample proportion tests

Test of two proportions

prop_test() for two samples

Chi-square test of independence

The chi-square distribution

How many tails for chi-square tests?

Performing a chi-square test

Chi-square goodness of fit tests

Visualizing goodness of fit

Performing a goodness of fit test

Proportion Tests

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

Assumptions in hypothesis testing

Common assumptions of hypothesis tests

Testing sample size

The "There is only one test" framework

There is only one test

Specifying and hypothesizing

Continuing the infer pipeline

Generating & calculating

Observed statistic and p-value

Non-parametric ANOVA and unpaired t-tests

Simulation-based t-test

Rank sum tests

Congratulations!

Non-Parametric Tests

Late Shipments w/ Freight Cost Group

StackOverflow Survey

Democratic Presidential Candidates by County

Late shipments Bootstrap Distribution

Late Shipments

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.

<h2>Découvrez les tests d’hypothèse dans R </h2>
Les tests d'hypothèse vous permettent de répondre à des questions sur vos ensembles de données d'une manière statistiquement rigoureuse. Dans ce cours, vous apprendrez comment et quand utiliser des tests courants tels que les tests t, les tests de proportion et les tests du khi-deux. 
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Vous développerez une compréhension approfondie de leur fonctionnement et des hypothèses qui les sous-tendent. Vous apprendrez aussi comment différents tests d’hypothèse sont liés grâce au cadre « Il n’y a qu’un seul test » et utiliserez des tests non paramétriques qui vous permettent de contourner les exigences des tests d’hypothèse traditionnels.
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<h2>Découvrez les tests t et les tests du khi carré</h2>
Vous commencerez par apprendre pourquoi les tests d’hypothèses dans R sont utiles tout en examinant certains concepts clés au fur et à mesure. Vous apprendrez aussi comment les tests t peuvent vous aider à vérifier les différences de moyennes entre deux groupes et comment les tests du chi carré peuvent vous aider à comparer les résultats observés aux résultats attendus.  
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<h2>Comprendre les relations entre les tests d’hypothèse en R</h2>
Au fur et à mesure de votre progression, vous découvrirez les relations entre différents tests, en explorant les éléments de hasard, l’indépendance des observations et les tailles d’échantillon. 
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À la fin de ce cours, vous aurez une compréhension plus approfondie des tests d’hypothèse dans R et saurez quand il est approprié d’utiliser des tests précis sur vos données. 
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Tout au long du cours, vous explorerez une enquête auprès des utilisateurs de Stack Overflow et un ensemble de données sur les livraisons tardives de fournitures médicales.

Sampling in R

Découvrez les tests d'hypothèses en R : t-tests, tests de proportion et tests du chi-carré.

Hypothesis Testing in R

Apprenez quand et comment utiliser les tests d’hypothèse dans R, y compris les tests t, les tests de proportion et les tests du chi carré. 

Scientifique des données associé en R

Analyste de données  en R

Statisticien en R

Fondamentaux des statistiques en R

Rejecting the null hypothesis when in fact the null hypothesis is true.

Finding the defendant guilty when in fact the defendant was innocent.

Failing to reject the null hypothesis when in fact the null hypothesis is false

Finding the defendant not guilty when in fact the defendant did commit the crime

Rejecting the null hypothesis when in fact the null hypothesis is false.

Finding the defendant not guilty when in fact they were innocent

Type I and type II errors

Hypothesis Testing in R

Exercice interactif pratique