Z-Test vs T-Test  Differences Between Z-Test and T-Test

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 known.

Z-tests and t-tests are the two statistical methods that involve data analysis, which has applications in science, business, and many other disciplines. The t-test can be referred to as a univariate hypothesis test based on t-statistic, wherein the mean, i.e., the average is known, and population variance, i.e., the standard deviation, is approximated from the sample. On the other hand, Z-test, also a univariate test which is based on a .

For eg:
Source: Z-Test vs T-Test (wallstreetmojo.com)

Uses

#1 – Z-Test

, as mentioned earlier, are the statistical calculations that can be used to compare population averages to a sample’s. The z-test will tell you how far, in terms, a data point is from the average of a data set. A z-test will compare a sample to a defined population that is typically used for dealing with problems relating to large samples (i.e., n > 30). Mostly, they are very useful when the standard deviation is known.

#2 – T-Test

are also calculations that can be used to test a hypothesis, but they are very useful when we need to determine if there is a statistically significant comparison between the 2 independent sample groups. In other words, a t-test asks whether the comparison between the averages of 2 groups is unlikely to have occurred due to random chance. Usually, t-tests are more appropriate when dealing with problems with a limited (i.e., n < 30).

Z-Test vs. T-Test Infographics

Here we provide you with the top 5 differences between the z-test vs. t-test you must know.

For eg:
Source: Z-Test vs T-Test (wallstreetmojo.com)

Key Differences

• One of the essential conditions for conducting a t-test is that population standard deviation or the variance is unknown. Conversely, the , as stated above, should be assumed to be known or be known in the case of a z-test.
• The t-test, as mentioned earlier, is based on student’s . On the contrary, the z-test depends upon the assumption that the distribution of sample means will be normal. Both the normal distribution and student’s t-distribution appears the same, as both are and symmetrical. However, they differ in one of the cases that in at-distribution, there is lesser space in the center and more in their tails.
• Z-test is used as given in the above table when the sample size is large, which is n > 30, and the t-test is appropriate when the size of the sample is not big, which is small, i.e., that n < 30.

Conclusion

By and to the larger extent, both these tests are almost similar, but the comparison comes only to their conditions for their application, meaning that the t-test is more appropriate and applicable when the size of the sample is not more than thirty units. However, if it is greater than thirty units, one should use a z-test. Similarly, there are also other conditions, which will make it clear that which test is to be performed in a situation.

Well, there are also different tests like the f test, two-tailed vs. single-tailed, etc., statisticians must be careful while applying them after analyzing the situation and then deciding which one to use. Below is a sample chart for what we discussed above.

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