Hypothesis Testing in Python
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Start Course for Free4 Hours15 Videos50 Exercises12,323 Learners3750 XPData Analyst with Python TrackData Scientist with Python TrackStatistics Fundamentals with Python Track
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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 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.
- 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
In the following tracks
Data Analyst with PythonData Scientist with PythonStatistics Fundamentals with PythonCollaborators
Prerequisites
Sampling in Python
James Chapman
Curriculum Manager, DataCamp
James is a Curriculum Manager 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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