Z-test

Z-test

A Z-test is a statistical test used to determine whether two population means are different when the variances are known and the sample size is large. The Z-test has a single outcome variable of interest, and the population from which samples are drawn should be normally distributed.

Assumptions

The Z-test assumes that the data points are independent of each other. In other words, the occurrence of one data point does not affect the occurrence of another data point. The Z-test also assumes that the data are normally distributed, but as the sample size gets larger, the Z-test becomes less sensitive to normality thanks to the Central Limit Theorem.

Types of Z-tests

There are several types of Z-tests, including:

• One-sample Z-test: This test is used when you want to know whether your sample comes from a particular population.
• Two-sample Z-test: This test is used when you want to know whether two samples come from the same population.
• Paired Z-test: This test is used when you have paired data.

Z-score

The Z-score is a measure of how many standard deviations an element is from the mean. It is calculated by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation.

See also

• Statistical hypothesis testing
• Student's t-test
• Central Limit Theorem

References

Z-test Resources
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