Everything about Z-Test

Z-Test: It is a statistical test used to determine whether the two population means are different when the variances are known, and the sample size is large. The test statistic is assumed to have the normal distribution, and nuisance parameters such as standard deviation should be known for an accurate z test to be performed.

Another definition of Z-test: A Z-test is a type of hypothesis test. Hypothesis testing is just the way for you to figure out if results from a test are valid or repeatable. For example, if someone said they had found the new drug that cures cancer, you would want to be sure it was probably true. A hypothesis test will tell you if it’s probably true or probably not true. A Z test is used when your data is approximately normally distributed.

Z-Tests Working: Tests that can be conducted as the z-tests include a one-sample location test, a two-sample location test, a paired difference test, and a maximum likelihood estimate. Z-tests are related to t-tests, but t-tests are best performed when an experiment has a small sample size. Also, T-tests assume the standard deviation is unknown, while z-tests assume that it is known. If the standard deviation of the population is unknown, then the assumption of the sample variance equaling the population variance is made. When we can run the Z-test: Different types of tests are used in the statistics (i.e., f test, chi-square test, t-test).

You would use a Z test if Your sample size is greater than 30. Otherwise, use a t-test.  Data points should be independent of each other. Some other words, one data point is not related or doesn’t affect another data point. Your data should be normally distributed. However, for large sample sizes (over 30), this doesn’t always matter.  Your data should be randomly selected from a population, where each item has an equal chance of being selected.  Sample sizes should be equal, if at all possible.