Course
Hypothesis Testing in Python
IntermediateSkill Level
Updated 12/2025PythonProbability & Statistics4 hr15 videos50 Exercises3,750 XP58,153Statement of Accomplishment
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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
Sampling in Python1
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.
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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FAQs
Is this course suitable for beginners?
This is an intermediate-level Python course. You should be comfortable with pandas, basic Python, and introductory statistics before starting.
What types of hypothesis tests does this course cover?
You will learn t-tests, proportion tests, chi-square tests, ANOVA, and non-parametric tests, along with the assumptions behind each one.
What datasets are used in this course?
You work with real-world data including Stack Overflow user feedback and supply-chain data for medical supply shipments.
When would I use hypothesis testing in my work?
Hypothesis testing is used by data analysts and data scientists to make statistically rigorous decisions, such as comparing group performance or validating A/B test results.
How long does this course take to complete?
The course has 4 chapters and 50 exercises. Most learners finish it in about 3.5 hours.
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