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Chapter 2
2.4. Statistical Analysis
Statistical analysis was performed with IBM SPSS version 20 (Statistical
Package for Social Sciences co-branded by International Business Machines
corporation). The data was first assessed to determine if the distribution
was parametric or non-parametric. As a result, the subsequent tests
performed were based on this criterion.
Parametric data is frequently referred to as normal data as it infers the
data follows a normal (Gaussian) distribution [184]. Normal distribution is
based on the assumption that 95% of all the data points fall within 1.96 of
the standard deviation (SD) of the mean. Parametric tests include t-tests
(comparing 1/2 groups) and ANOVA (analysis of variance) which can
analyse the variance of multiple groups simultaneously. Parametric data
are considered more powerful as all the data fits within the 1.96±SD. Nonparametric
data do not fit any ordered distribution. Tests for nonparametric
data are also referred to as distribution-free methods. Methods
of analyzing non-parametric data include the Mann-Whitney U test (2
comparisons) and the Kolmogorov-Smirov Z test for multiple comparisons
[184].
Comparing groups to evaluate if there are differences between them is
usually performed by finding a p value. The null hypothesis (H 0 ) is that the
result is likely to have occurred by chance and that given a larger set of
values the same results would not occur. The alternative hypothesis (H A )
states that the result is unlikely to have arisen by chance and that given a
larger dataset this result would still be statistically significant. For example
if a null hypothesis was set that there was no difference between series of
observations of A and a series of observation of B and the alternative
hypothesis would be there was a difference between them. If the resulting
p values from either parametric or non parametric tests was