1.0 PARAMETRIC ANALYSIS vs NON-PARAMETRIC ANALYSIS
Parametric analysis is used for fairly large samples that are normally distributed. Non-parametric
analysis is used for small samples. It does not require that the data be normally distributed.
2.0 PARAMETRIC ANALYSIS OF CONTINUOUS DATA
Inference on numeric continuous data is based on the comparison of sample means.
Two test statistics are commonly used: t- and F-statistics.
The t-statistic is for 2 samples.
The F statistic is used for 3 or more samples.
The student t-test is the most commonly used test statistic for inference on continuous
numerical data. It is defined for independent and paired samples. It is used uniformly for sample sizes below 60 and for larger
samples if the population standard deviation is not known.
The F-test is a generalized test used in inference on 3 or more sample means in procedures
called analysis of variance, ANOVA.
Each of the tests has its own formulas. The tests yield a p-value that is used
to make conclusions about the null hypothesis.
3.0 P VALUE
p-value is the end-product of data analysis. If p<0.05 the null hypothesis is rejected. If p > 0.05 the null hypothesis
is not rejected. P-value is the probability of rejecting a ‘valid’ null hypothesis by mistake. The slower the
p-value, the stronger the conclusion from the data analysis.