Differences Between ZTest and TTest
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.
Ztests and ttests are the two statistical methods that involve data analysis, which has applications in science, business, and many other disciplines. The ttest can be referred to as a univariate hypothesis test based on tstatistic, 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, Ztest, also a univariate test which is based on a standard normal distribution.
Uses
#1 – ZTest
Ztest Formula, as mentioned earlier, are the statistical calculations that can be used to compare population averages to a sample’s. The ztest will tell you how far, in standard deviations terms, a data point is from the average of a data set. A ztest 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 – TTest
Ttests 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 ttest asks whether the comparison between the averages of 2 groups is unlikely to have occurred due to random chance. Usually, ttests are more appropriate when dealing with problems with a limited sample size (i.e., n < 30).
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ZTest vs. TTest Infographics
Here we provide you with the top 5 differences between the ztest vs. ttest you must know.
Key Differences
 One of the essential conditions for conducting a ttest is that population standard deviation or the variance is unknown. Conversely, the population variance formula, as stated above, should be assumed to be known or be known in the case of a ztest.
 The ttest, as mentioned earlier, is based on student’s tdistribution. On the contrary, the ztest depends upon the assumption that the distribution of sample means will be normal. Both the normal distribution and student’s tdistribution appears the same, as both are bellshaped and symmetrical. However, they differ in one of the cases that in atdistribution, there is lesser space in the center and more in their tails.
 Ztest is used as given in the above table when the sample size is large, which is n > 30, and the ttest is appropriate when the size of the sample is not big, which is small, i.e., that n < 30.
ZTest vs. TTest Comparative Table
Basis  Z Test  TTest  
Basic Definition  Ztest is a 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 ttest can be referred to as 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 



Based upon (a type of distribution)  Based on Normal distribution.  Based on Studentt distribution. 
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 ttest 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 ztest. 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, twotailed vs. singletailed, 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.
Recommended Articles
This article has been a guide to ZTest vs. TTest. Here we discuss the top 5 differences between these hypothesis testing along with infographics and a comparative table. You may also have a look at the following articles –
 What is Hypothesis Testing?
 Formula of FTest
 Logical Test in Excel (AND, OR, IF)
 How to do FTest in Excel?
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