Why are Nonparametric Procedures Necessary?
Some researchers do not believe nonparametric procedures are robust enough to yield reliable results. This may hold true if a sample is small or variances between groups are unequal. But does that make the procedures themselves unreliable, or are they simply as reliable as the data themselves?
Consider a small, hypothetical quantitative case study, and develop an original response as follows:
Describe the hypothetical study, and name two variables to compare or correlate.
Propose a sample size. Which nonparametric statistical procedures would you apply and why?
Why Are Nonparametric Procedures Necessary?
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In the hypothetical study considered for this discourse, the independent variable is age, and the dependent variable is height. The age of individuals will be accounted for in years, whereas their corresponding heights will be donated in centimeters. The sample size of case studies is determined by statistical test power, significance level, and the expected effect size (Wubetie, 2019). Being that this is a non-parametric test, 30 participants can be used to approximate normality comfortably. Age and height are continuous variables but are not always known to follow a normal distribution. In this vein, non-parametric statistical procedures are suitable