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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.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
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Start Course for FreeWhat you'll learn
- Differentiate between Type I and Type II errors and their consequences for statistical conclusions
- Distinguish between parametric and non-parametric approaches based on assumptions of normality, sample size, and independence
- Evaluate p-values, confidence intervals, and standardized test statistics produced by Python libraries to determine whether to reject the null hypothesis at a specified alpha
- Identify the suitable hypothesis test in Python (z-test, t-test, ANOVA, proportion test, chi-square, or non-parametric) for a given research question, data type, and sample conditions
- Recognize the correct null and alternative hypotheses, significance level, and tail direction for typical analytical scenarios
Prerequisites
Curriculum
Course outline
1
Hypothesis Testing Fundamentals
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-scores50 XP
- Uses of A/B testing50 XP
- Calculating the sample mean100 XP
- Calculating a z-score100 XP
- p-values50 XP
- Criminal trials and hypothesis tests50 XP
- Left tail, right tail, two tails100 XP
- Calculating p-values100 XP
- Statistical significance50 XP
- Decisions from p-values50 XP
- Calculating a confidence interval100 XP
- Type 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.
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.
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.
Hypothesis Testing in Python
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