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Limitations and Requirements of ANOVA - Essay Example

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Where the number of samples are more than two ANOVA on its own is incomplete. It is impossible to determine with certainty whether the intermediates differ significantly from either extremes. For this reason the…
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Limitations and Requirements of ANOVA
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Download file to see previous pages The samples’ variances should not be different though some departures can be accommodated. All individuals used in the samples must be selected randomly from the population. All individuals of the samples must have equal probability for being selected. The sizes of the sample should be equal but there is an allowance of some differences. One of the limitations of ANOVA is that, when a significant data difference cannot be found, the samples cannot be said to be the same. It only indicates differences between groups and not groups which are different.
Normality assumes that the errors which are random within each group of treatment, the groups’ mean deviations, have a normal probability distribution. For normal data but variances which are heterogeneous, ANOVA is good for balanced designs but not for designs which are highly unbalanced. In normal data setting, heterogeneous variances and designs which are unbalanced, Welch’s ANOVA might be used for the accommodation of unequal variances. With variances which are homogenous but data which is non-normal, ANOVA is good for designs which are balanced with large samples. It is not good for unbalanced designs with small samples. In non-normal data setting, variances which are homogenous and a small sample or unbalanced design, a non-parametric procedure is preferred. If the distribution of data is not normal and heterogeneity of variances exist, there might be transformation necessity. The importance of a design which is balanced and existence of a large sample must be put into consideration. A common standard deviation is shared by all normal distributions.
The different t-test options can be used around the equal variances assumptions or unequal variances assumption. The f-test, apart from being used to for t-tests, it can also be used to compare variations in two data sets in the CJ data. The test makes use of a calculated F stat ...Download file to see next pagesRead More
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