# nonparametric

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## Words related to nonparametric

### not involving an estimation of the parameters of a statistic

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Data analysis is based on non-parametric statistics. The results indicate that the base of the pyramid consumers is characterized by adopting incremental innovations tardily, as pointed out by the literature.
Non-parametric statistics and tests were used in the bivariate analysis including Spearman's rho, the Mann-Whitney U test (2 groups), and the Kruskall-Wallis test (3 or more groups) to examine associations between the SWBS total score and its subscales and to test for significant differences between groups in the demographic variables.
Since data in the current study (Riley & Organist, 2014) were ordinal, much of it was analyzed with non-parametric statistics. Thus, the concepts of outcomes studies, secondary analysis, and nonparametric statistical tests used for data analysis will be the focus of this review.
References from similar research and the reference for non-parametric statistics are included in the Bibliography section.
The proposal to use Non-Parametric Statistics as a resource for the analysis of information that does not require a population model and that does not require a large number of hypotheses takes into consideration that this resource also provides the Didactic Engineering methodology with a treatment that meets the prerogative of falsifiability in Popper's scientific method.
The topics covered are the expected ones for an introductory course, such as probability, sample variability, statistical inferences involving one and two populations, applications of chi-square, linear correlation and regression analysis, and non-parametric statistics.
There is a brief chapter on descriptive statistics, e.g., mean and range, then a section on parametric statistics (t-test, regression), a section on non-parametric statistics and one that we seldom encounter where multivariate statistics are covered.
Because of the non-normal distribution of measured parameters, the authors performed non-parametric statistics, Kendall's tau, the Kruskal-Wallis one-way analysis of variance (ANOVA) (National Resource Conservation Service, 2002), and a multiple-comparison procedure of the Kruskal-Wallis test (Cabilio & Masaro, 2001) with SPSS 12 and Excel 2003.
Because the ANOVA assumptions of homogeneity of variances and normality of the variables were not met, even after several transformations, we used profile analysis, which can be performed with both parametric and non-parametric statistics to test for the category X time interactions (Ende 1993).
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