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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.
Yum, That Dish Tests GoodFree
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.To the lab for testing50 xpUses of A/B testing50 xpCalculating the sample mean100 xpCalculating a z-score100 xpA tail of two z's50 xpCriminal trials and hypothesis tests50 xpLeft tail, right tail, two tails100 xpCalculating p-values100 xpStatistically significant other50 xpDecisions from p-values50 xpCalculating a confidence interval100 xpType I and type II errors100 xp
Pass Me ANOVA Glass of Iced t
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.Is this some kind of test statistic?50 xpHypothesis testing workflow100 xpTwo sample mean test statistic100 xpTime for t50 xpWhy is t needed?50 xpThe t-distribution50 xpFrom t to p100 xpPairing is caring50 xpIs pairing needed?100 xpVisualizing the difference100 xpUsing ttest()100 xpP-hacked to pieces50 xpVisualizing many categories100 xpANOVA100 xpPairwise t-tests100 xp
Letting the Categoricals Out of the Bag
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.Difference strokes for proportions, folks50 xpt for proportions?50 xpTest for single proportions100 xpA sense of proportion50 xpTest for two proportions100 xpproportions_ztest() for two samples100 xpDeclaration of independence50 xpThe chi-square distribution50 xpHow many tails for chi-square tests?50 xpChi-square test of independence100 xpDoes this dress make my fit look good?50 xpVisualizing goodness of fit100 xpChi-square test of goodness of fit100 xp
Time to Define the Relationship
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.
PrerequisitesSampling in Python
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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