The
Analysis Of Variance, popularly known as the ANOVA, can be used in cases where
there are more than two groups.
When we have only two samples we can use the
t-test to compare the means of the samples but it might become unreliable in
case of more than two samples. If we only compare two means, then the t-test
(independent samples) will give the same results as the ANOVA.
It is used to compare
the means of more than two samples. This can be understood better with the help
of an example.
One Way ANOVA
EXAMPLE: Suppose we want to test the effect of five different exercises.
For this, we recruit 20 men and assign one type of exercise to 4 men (5
groups). Their weights are recorded after a few weeks.
We may find out whether the effect of these exercises on them is
significantly different or not and this may be done by comparing the weights of
the 5 groups of 4 men each.
The example above is a
case of one-way balanced ANOVA.
It has been termed as
one-way as there is only one category whose effect has been studied and
balanced as the same number of men has been assigned on each exercise. Thus the
basic idea is to test whether the samples are all alike or not.
There are four basic assumptions used in
ANOVA.
= the expected values of the errors are zero
= the variances of all errors are equal to each other
= the errors are independent
= they are normally distributed
Source : http://explorable.com/anova.html
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