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    Non-Parametric Tests in Business

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    What are examples in which non-parametric tests are used to analyze business related problems in general, and more specifically, problems related to marketing?

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    Nonparametric tests are often used in place of their parametric counterparts when certain assumptions about the underlying population are questionable. For example, when comparing two independent samples, the Wilcoxon Mann-Whitney test does not assume that the difference between the samples is normally distributed whereas its parametric counterpart, the two sample t-test does. Nonparametric tests may be, and often are, more powerful in detecting population differences when certain assumptions are not satisfied. All tests involving ordinal or ranked data, i.e. data that can be put in order, are nonparametric (http://www.stats.gla.ac.uk/steps/glossary/nonparametric.html#nonparat).
    For example, placing brands of cooking oil in order of preference is ordinal data, which would be analyzed using nonparametric tests.
    Examples of Non-parametric Test:
    • e.g., Chi-Square, Kruskal Wallis, Friedman tests and Wilcoxon Rank Sum test
    • Simple linear (bivariate) regression and correlations (and related non-parametric techniques) (e.g., Spearman rank correlation)
    Non-parametric tests supply a set of functions associated with the flow of goods, information. Companies increasingly view statistics as a crucial element in their corporate strategy. Nonparametric tests collect data on problems such as:
    1. Customer satisfaction,
    2. Customer demands for responsiveness,
    3. Customization,
    4. Quality, and value.
    In other words, we learn how customer satisfaction, demand for responsiveness, quality and value impact business and the bottom-line. For example, anticipating the future is a major part of any management role; it is the basis of many strategies. Being able to predict more accurately than a competitor is a huge competitive advantage, therefore changes in statistics that improve our ability to forecast or to predict likely outcomes are sources of competitive advantage. So the relevance for business is that statistics help to create new tools for being able to forecast the future, placing you in a much better position than your competitors, and that’s the bottom line. (1)
    Marketing Research
    Marketing research can be concerned with any of a variety of aspects of the market: the product, sales (e.g. analyzing sales response), buyer behavior, promotion, ...

    Solution Summary

    This solution provides examples in which non-parametric tests are used to analyze business related problems in general, and more specifically, problems related to marketing.