The Chi Square (X
2
) test is undoubtedly the most important and most used member of the
nonparametric family of statistical tests (Aron, Coups, and Aron, 2011). Chi Square is
employed to test the difference between an actual sample and another hypothetical or
previously established distribution such as that which may be expected due to chance or
probability. Chi Square can also be used to test differences between two or more actual
samples.
The OneWay Classification (or sometimes referred to as the Single Sample Chi Square
Test) is one of the most frequently reported nonparametric tests in journal articles
(Choudhury, 2009). The test is used when a researcher is interested in the number of
responses, objects, or people that fall in two or more categories. This procedure is
sometimes called a goodnessoffit statistic. Goodnessoffit refers to whether a
significant difference exists between an observed number and an expected number of
responses, people or objects falling in each category designated by the researcher. The
expected number is what the researcher expects by chance or according to some null
hypothesis.
The TwoWay Chi Square is a convenient technique for determining the significance of
the difference between the frequencies of occurrence in two or more categories with two
or more groups (Choudhury, 2009).
The Chi Square Test for Single Variance is used to test a hypothesis on a specific value
of the population variance (Choudhury, 2009). Statistically speaking, we test the null
hypothesis H
0
: σ = σ
0
against the research hypothesis H
1
: σ # σ
0
where σ is the population
mean and σ
0
is a specific value of the population variance that we would like to test for
acceptance.
In other words, this test enables us to test if the given sample has been drawn
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 Spring '11
 DONNAWALL
 Variance, Chisquare distribution, CHI SQUARE

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