T-Test or Z-Test for Python: Which Should You Use?
Whether you're analyzing user behavior, testing a business hypothesis, or comparing group performance, choosing the right statistical test can dramatically shape the conclusions you draw. This guide focuses on two foundational tools in hypothesis testing: the T-test and the Z-test.
We’ll walk you through when and how to apply each test, but also go deeper to equip you with the theoretical understanding behind them. You’ll not only implement these tests using Python, but also learn about their assumptions, strengths, and typical real-world applications.
What Will You Learn from This Guide?
- Theoretical understanding of T-tests and Z-tests
- When to use each based on sample size, variance, and test type
- Coding walkthroughs in Python using scipy.stats and statsmodels
- A conceptual question at the end to reinforce your understanding - no coding required
Who Is This Guide For?
- Aspiring data scientists and analysts looking to build strong statistical foundations
- Professionals transitioning from business intuition to data-driven decision making
- Learners who know some Python and want to apply it to real-world scenarios
- Anyone asking: “Should I use a T-test or a Z-test here?”
If you're aiming to not just run the tests but to understand why you're using them, and what the results truly mean, then you’re in the right place. Let’s dive in.