by Albert Anthony D. Gavino
Parametric vs Non-Parametric, Small Data vs Big Data,
Who is the more superior race?
The traditional "parametric" tests, such as t-tests and the analysis of variance, assume the population(s) to be normally distributed; they generally assume that one's measures derive from an equal-
Non-parametric tests involve non-normal distributions, some of which are the following:
- multi forms of chi-
square tests
- Fisher Exact Probability test
- Mann-
Whitney Test
- Wilcoxon Signed-
Rank Test
- Kruskal-Wallis Test
- and the Friedman Test
In the field of Big Data, Non-parametric is the higher science and the more powerful one, as noted by one of the UP professors.
We no longer assume that distributions are normal and we can’t use t-tests or ANOVA for that matter.
