Inferential Statistical Techniques: Parametric And Non-Parametric Techniques
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
To watch the video practical applications, go to my Youtube Channel.
Youtube Username is: Obezip Universal Statisticals
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