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F2 test
These tests were based on the assumption that the samples were drawn from normally distributed populations, or more accurately that the sample means were normally distributed. Since the testing procedure requires assumption about the type of population or parameters, i.e. population values, these tests are known as ‘parametric tests’.

There are many situations in which it is not possible to make any right assumption about the distributions of the population from which samples are being drawn. This limitation has led to the development of a group of alternative techniques known as non-parametric tests are used, no assumption about the parameters of the population or populations from which we draw our samples are made. Chi-square test of independence and goodness of fit discussed here with the use of non-parametric tests.

Though non-parametric theory developed as early as the middle of the nineteenth century, it was only after 1945 that non-parametric test came to be used widely. Originated in sociological and psychological research, non-parametric tests today are very popular in behavioral sciences. The following three reasons account for the increasing use of non-parametric tests in business research.

Some of its main topics are:

1. χ2 defined
2. Degrees of freedom
3. Uses of χ2 test
4. Statistical inference
5. Arithmetic means of X and Y
6. Deviations from assumed means

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