Z-Test Vs T-Test
Some of the common difference between Z-test and T-test are:
|
Aspect |
T-Test |
Z-Test |
|---|---|---|
|
Purpose |
Compare means of small samples (n < 30) |
Compare means of large samples (n ≥ 30) |
|
Assumptions |
Normally distributed data, approximate normality |
Normally distributed data, known population standard deviation |
|
Population Standard Deviation |
Unknown |
Known |
|
Sample Size |
Small (n < 30) |
Large (n ≥ 30) |
|
Test Statistic |
T-distribution |
Standard normal distribution (Z-distribution) |
|
Degrees of Freedom |
n1 + n2 – 2 |
Not applicable |
|
Use Case |
Small sample analysis, comparing means between groups |
Large sample analysis, population mean comparisons |
|
One-Sample vs. Two-Sample |
Both |
Usually two-sample |
|
Data Requirement |
Raw data |
Raw data |
|
Complexity |
Relatively more complex |
Relatively simpler |
Difference between Z-Test and T-Test
Z-tests are used when the population variance is known and the sample size is large, while t-tests are used when the population variance is unknown and the sample size is small.
This article explains the differences between Z-tests and T-tests, detailing their purposes, assumptions, sample size requirements, and applications in statistical hypothesis testing.
Table of Content
- What is Z-test?
- Types of Z-Test
- What is T-test?
- Types of T-Tests
- Difference between Z-Test and T-Test
- FAQs: Z-Test Vs T-Test
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