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.
Difference Between Z Test and T-Test
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 a univariate hypothesis test that is based on t-statistic, wherein the mean i.e. the average is known, and population variance i.e. standard deviation is approximated from the sample. On the other hand, Z-test, also a univariate test which is based on a standard normal distribution.
Use of the Z Test
Z-tests, 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 standard deviations terms, a data point is from the average of a data set. A z-test will do a comparison of 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.
Use of T-Test
T-tests 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 you are dealing with problems with a limited sample size (i.e. n < 30).
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Z Test vs T Test Infographics
Here we provide you with the top 5 difference you must know.
- One of the most important conditions for conducting a t-test is that population standard deviation or the variance is unknown. Conversely, population variance as stated above should be assumed to be known or be known in case of a z-test.
- The t-test as mentioned earlier is based on Student’s t-distribution. 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 bell-shaped 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.
Head to Head Comparison
|Basic Definition||Z-test is kind of hypothesis test which ascertains if the averages of the 2 datasets are different from each other when standard deviation or variance is given.||The t-test can be referred to a kind of parametric test that is applied to an identity, how the averages of 2 sets of data differ from each other when the standard deviation or variance is not given.|
|Population Variance||The Population variance or standard deviation is known here.||The Population variance or standard deviation is unknown here.|
|Sample Size||The Sample size is large||Here the Sample Size is small.|
|Key Assumptions||1) All data points are independent. 2) Normal Distribution for Z, with an average zero and variance = 1.||1) All data points are not dependent.
2) Sample values are to be recorded and taken accurately
|Based upon (a type of Distribution)||Z test is based on Normal distribution.||A t-test is based on Student-t distribution.|
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 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.
This has been a guide to them Z Test vs T-Test. Here we discuss the top 5 differences along with infographics and comparison table. You may also have a look at the following articles –