Congratulations!
1. Congratulations!
Well done! You finished the course.2. You learned things
In Chapter 1, you learned the workflow for testing a proportions against a hypothesized value. You calculated the observed statistic, and a z-score, then transformed the z-score to get a p-value. You also learned about false negative and false positive errors. In Chapter 2, you learned 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. In Chapter 3, we returned to proportion responses, and you learned how to test for differences in proportions between two groups using proportion tests. You then extended it to more than two groups with chi-square independence tests, and returned to the one sample case with chi-square goodness of fit tests. In Chapter 4, you learned about the assumptions made by parametric hypothesis tests, and saw how simulation-based and rank-based non-parametric tests can be used when those assumptions aren't met.3. More courses
Hypothesis testing is part of a branch of statistics known as inference. DataCamp has more courses that delve deeper into inference using infer. The techniques used in this course are an example of frequentist statistics. A rival paradigm is known as Bayesian statistics, and naturally, DataCamp has a series of courses that teach it, beginning with the fundamentals. The course began with an example of A/B testing, which is a common use case for hypothesis testing. You can learn more in the dedicated course.4. Let's practice!
I hope you enjoyed this course. Happy learning!Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.