Difference between Z-test, F-test, and T-test

A z-test is used for testing the mean of a population versus a standard, or comparing the means of two populations, with large (n ≥ 30) samples whether you know the population standard deviation or not. It is also used for testing the proportion of some characteristic versus a standard proportion, or comparing the proportions of two populations.
Example:Comparing the average engineering salaries of men versus women.
Example: Comparing the fraction defectives from 2 production lines.

A t-test is used for testing the mean of one population against a standard or comparing the means of two populations if you do not know the populations’ standard deviation and when you have a limited sample (n < 30). If you know the populations’ standard deviation, you may use a z-test.
Example:Measuring the average diameter of shafts from a certain machine when you have a small sample.

An F-test is used to compare 2 populations’ variances. The samples can be any size. It is the basis of ANOVA.
Example: Comparing the variability of bolt diameters from two machines.

Matched pair test is used to compare the means before and after something is done to the samples. A t-test is often used because the samples are often small. However, a z-test is used when the samples are large. The variable is the difference between the before and after measurements.
Example: The average weight of subjects before and after following a diet for 6 weeks


27 thoughts on “Difference between Z-test, F-test, and T-test

  1. Hi , thanks for this explanation . It helped , but i still have one doubt. Can Z test be still used for comparing proportion of two samples when sample size is less than 30 ? for example i need to compare the conversion rate of two campaigns for 15 days . can i use Z test here ?

  2. Hello.

    When comparing the average engineering salaries of men versus women, why would we use a z-test—as opposed to just comparing the averages for men versus women?

    • Direct comparison of two averages doen’t account for the variance of the individual data. One group could have a very small distribution of incomes and the other much larger. Then it is possible that the average of the small variance group lies within the distribution of the large variance group and that the difference between the averages isn’t actually meaningful.

  3. Appreciating the persistence you put into your blog and in depth information you present.
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  4. Hello.

    Somewhere I’ve read that Anova is used instead of t-test to test whether the means of several groups (more than two) are equal.
    So, is it used to compare means or variances?

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