Hypothesis Testing in Python
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
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Course Description
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, you'll gain a deep understanding of how these tests work, and the key assumptions that underpin them. You'll also discover how different tests are related using the “there is only one test" framework, before learning how to use non-parametric tests to go beyond the limitations of side-step the requirements of hypothesis tests.
- 1
Introduction to Hypothesis Testing
FreeHow 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-scores50 xpUses of A/B testing50 xpCalculating the sample mean100 xpCalculating a z-score100 xpp-values50 xpCriminal trials and hypothesis tests50 xpLeft tail, right tail, two tails100 xpCalculating p-values100 xpStatistical significance50 xpDecisions from p-values50 xpCalculating a confidence interval100 xpType I and type II errors100 xp - 2
Two-Sample and ANOVA Tests
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-tests50 xpHypothesis testing workflow100 xpTwo sample mean test statistic100 xpCalculating p-values from t-statistics50 xpWhy is t needed?50 xpThe t-distribution50 xpFrom t to p100 xpPaired t-tests50 xpIs pairing needed?100 xpVisualizing the difference100 xpUsing ttest()100 xpANOVA tests50 xpVisualizing many categories100 xpConducting an ANOVA test100 xpPairwise t-tests100 xp - 3
Proportion 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 tests50 xpt for proportions?50 xpTest for single proportions100 xpTwo-sample proportion tests50 xpTest of two proportions100 xpproportions_ztest() for two samples100 xpChi-square test of independence50 xpThe chi-square distribution50 xpHow many tails for chi-square tests?50 xpPerforming a chi-square test100 xpChi-square goodness of fit tests50 xpVisualizing goodness of fit100 xpPerforming a goodness of fit test100 xp - 4
Non-Parametric 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 testing50 xpCommon assumptions of hypothesis tests50 xpTesting sample size100 xpNon-parametric tests50 xpWhich parametric test?50 xpWilcoxon signed-rank test100 xpNon-parametric ANOVA and unpaired t-tests50 xpWilcoxon-Mann-Whitney100 xpKruskal-Wallis100 xpCongratulations!50 xp
Collaborators
Prerequisites
Sampling in Python
James Chapman
Content Developer, DataCamp
James is a Content Developer at DataCamp. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in quasar detection and tutored Math and English. He joined DataCamp as a learner in 2018, and the data skills learned on DataCamp were quickly integrated into his scientific projects. In his spare time, he enjoys restoring retro toys and electronics.
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