A Comparison of Parametric, Nonparametric, Inferential & Multivariate

This essay provides a comparative analysis of parametric and non-parametric statistical methods, alongside inferential and multivariate analysis. It highlights that parametric tests require power analysis, while non-parametric tests do not. The choice between these methods depends on the level of data measurement, with interval data being the minimum requirement to distinguish between them. Inferential statistics, considered the most robust, are matched with research questions and hypotheses. Multivariate statistics are used to test associations among more than two variables. The essay concludes that non-parametric tests are suitable for nominal or ordinal data, while parametric tests are used to test means and non-parametric tests are used for medians. Inferential statistics are essential for estimating parameters and testing hypotheses, and multivariate analysis is valuable for representing data distributions and statistical inference.

Running head: METHODS OF STATISTICS
Nonparametric versus Parametric, Inferential and Multivariate Analysis
Name of the Student:
Name of the University:
Author’s note:

1METHODS OF STATISTICS
Table of Contents
Introduction:....................................................................................................................................2
Discussion:.......................................................................................................................................2
Conclusion:......................................................................................................................................2
References:-.....................................................................................................................................2

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Introduction:
In this short essay, the comparative study between parametric and non-parametric as well
as statistical methods regarding inferential and multivariate analysis is discussed.
Discussion:
The main difference of parametric and non-parametric analysis is that non-parametric
need no power analysis, whereas non-parametric tests need not power analysis, whereas
parametric tests need a power analysis. At the time of decision-making in case of eventual
transferability versus generalizability of findings, it is a concerning issue to choose the most
robust and maximum level of statistical analysis that goes with the measurement level.
Therefore, a minimum level of interval data is required to distinct the parametric and non-
parametric analysis. Various types of opinions such as- 1) nominal data equal to categorical data
2) ordinal data equal to ranked data 3) interval data equal scaled data 4) ratio data equal weighted
data, indicate most robust statistics to classify the inferential statistics.
The most robust statistics are grouped as inferential statistics. Relying on the
measurement level, the proper inferential statistics should be matched with research questions,
research hypotheses, levels of data and chosen statistics for testing.
To test the association among more than two variables, multivariate statistic is used.
Evidence based quality management recognizes the fitness of non-parametric versus parametric,
inferential and multivariate analysis for critical evidence appraisal.

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Conclusion:
Following the nominal or ordinal (Likert scale) distribution of data, non-parametric tests
are required. More of it, parametric analysis is to test the means of the groups whereas non-
parametric analysis to used to the medians of the groups (Sheskin, 2011). Especially, for ranked
data, ordinal data and outliers, non-parametric tests are essential (Weaver et al., 2018).
Commonly, statistics of inference devises inference and conclusions about a population
on the basis of a sample of data gathered from the population. Estimating parameters and testing
of hypotheses are executed with the help of inferential statistics (Eugster, Hothorn and Leisch,
2008).
Multivariate analysis could be strategically efficient if it represent the distributions of
observed data and become an integral segment of statistical inference especially where various
quantities are the interest of the same analysis (Hair et al., 1998).

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References:-
Eugster, M. J., Hothorn, T., & Leisch, F. (2008). Exploratory and inferential analysis of
benchmark experiments.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate
data analysis (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice hall.
Sheskin, D. J. (2011). Parametric Versus Nonparametric Tests. In International Encyclopedia of
Statistical Science (pp. 1051-1052). Springer Berlin Heidelberg.
Weaver, K. F., Morales, V., Dunn, S. L., Godde, K., & Weaver, P. F. (2018). Parametric versus
Nonparametric Tests. An Introduction to Statistical Analysis in Research: With
Applications in the Biological and Life Sciences, First, 191-194.