How does hypothesis testing work and what problems can it solve? To find out, you’ll walk through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-values, and false negative and false positive errors.

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 a confidence interval

False positive (Type I) errors

False negative (Type II) errors

Not an error

Type I and type II errors

Hypothesis Testing Fundamentals

In this chapter, you’ll learn how to test for differences in means between two groups using t-tests and 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 ttest()

ANOVA tests

Visualizing many categories

Conducting an ANOVA test

Pairwise t-tests

Two-Sample and ANOVA Tests

Now it’s time to test for differences in proportions between two groups using proportion tests. Through hands-on exercises, you’ll extend your proportion tests 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

proportions_ztest() 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

Finally, it’s time to learn about the assumptions made by parametric hypothesis tests, and see how non-parametric tests can be used when those assumptions aren't met.

Assumptions in hypothesis testing

Common assumptions of hypothesis tests

Testing sample size

Non-parametric tests

Which parametric test?

Wilcoxon signed-rank test

Non-parametric ANOVA and unpaired t-tests

Wilcoxon-Mann-Whitney

Kruskal-Wallis

Congratulations!

Non-Parametric Tests

Late Shipments

Stack Overflow

U.S. Democrat Votes 2012/2016

U.S. Republican Votes 2008/2012

Hypothesis testing lets you answer questions about your datasets in a statistically rigorous way. In this course, you'll grow your Python analytical skills as you learn how and when to use common tests like t-tests, proportion tests, and chi-square tests. Working with real-world data, including Stack Overflow user feedback and supply-chain data for medical supply shipments, you'll gain a deep understanding of how these tests work and the key assumptions that underpin them. You'll also discover how non-parametric tests can be used to go beyond the limitations of traditional hypothesis tests.

Sampling in Python

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

Hypothesis Testing in Python

Associate Data Scientist  in Python

Data Analyst  in Python

Statistics Fundamentals  in Python

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 Python

Hands-on interactive exercise