Inferential statistics describe the many ways in which statistics derived from observations on samples from study populations can be used to deduce whether or not those populations are truly different.

Statistical tests are categorized into two groups, namely parametric and non-parametric test. Non-parametric tests are tests that do not depend on knowledge of the population distribution or its parameters. They are statistical methods that require no assumption about the form of the probability distribution of the population and are often referred to as distribution-free methods.

Parametric statistics are those statistics that require that the population from which sample data is drawn must possess certain characteristics or conditions before they can be used. Parametric methods are statistical methods that begin with an assumption about the probability distribution of the population which is often that the population has a normal distribution.

Non-parametric Technique              Parametric Alternative

Chi-square for goodness of fit            None

Chi-square for independence              None

Mann-Whitney U Test                        Independent-samples t-test

Wilcoxon Signed Rank Test               Paired-samples t-test

Kruskal-Wallis Test                            One-way between-groups ANOVA

Friedman Test                                     One-way repeated-measures ANOVA

Spearman Rank Order Correlation     Pearson’s product-moment correlation

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