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

Late shipments Bootstrap Distribution

Democratic Presidential Candidates by County

StackOverflow Survey

Late Shipments w/ Freight Cost Group

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>Discover Hypothesis Testing in R </h2>
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. 
<br><br>
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.
<br><br>
<h2>Learn About T-Tests and Chi-Square Tests</h2>
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.  
<br><br>
<h2>Understand the Relationships Between R Hypothesis Tests</h2>
As you progress, you’ll discover the relationships between different tests, exploring elements of randomness, independence of observation, and sample sizes. 
<br><br>
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. 
<br><br>
Throughout the course, you'll explore a Stack Overflow user survey and a dataset of late shipments of medical supplies."

Sampling in R

Learn about hypothesis testing in R, including how and when you should use common tests like t-tests, proportion tests, and chi-square tests.

Hypothesis Testing in R

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests. 

Associate Data Scientist in R

Data Analyst  in R

Statistician in R

Statistics Fundamentals in 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

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