Z Test T Test. Differences between z test and t test. T test refers to a type of parametric test that is applied to identify how the means of two sets of data differ from one another when variance is not given.
T test refers to a type of parametric test that is applied to identify how the means of two sets of data differ from one another when variance is not given. Population mean is not the same as the sample mean. Z test implies a hypothesis test which ascertains if the means of two datasets are different from each other when variance is given.
We do not know the population variance but our sample size is large n 30.
Differences between z test and t test. A z test is used when the population parameters like standard deviation are known. Z test implies a hypothesis test which ascertains if the means of two datasets are different from each other when variance is given. Z test is the statistical hypothesis which is used in order to determine that whether the two samples means calculated are different in case the standard deviation is available and sample is large whereas the t test is used in order to determine a how averages of different data sets differs from each other in case standard deviation or the variance is not.
