Nonparametric Tests

When To Use Them

in Marketing

Statistical procedures are either parametric or nonparametric. Parametric statistical tests require assumptions about the population from which the samples are drawn. For example, many tests such as the t Test, Chi-Square tests, z Tests, and F tests, and many types of hypothesis tests require the underlying population to be normally distributed. Some tests require equal variances of both populations.

Sometimes these assumptions cannot be always be assumed. Examples of this would be if the population is highly skewed or if the underlying distribution or variances were entirely unknown.

Nonparametric tests have no assumptions regarding distribution of underlying populations or variance. Most of this are very easy to perform but they are not usually as precise as parametric tests and the Null Hypothesis usually requires more evidence to be rejected in a nonparametric test.

When To Use Nonparametric Tests

Nonparametric tests are often used as shortcut replacements for more complicated parametric tests. You can quite often get a quick answer that requires little calculation by running a nonparametric test.

Nonparametric tests are often used when the data is ranked but cannot be quantified. For example, how would you quantify consumer rankings such as very satisfied, moderately satisfied, just satisfied, less than satisfied, dissatisfied?

Nonparametric tests can be applied when there are a lot of outliers that might skew the results. Nonparametric tests often evaluate medians rather than means and therefore if the data have one or two outliers, the outcome of the analysis is not affected.

They come in especially handy when dealing with non-numeric data, such as having customers rank products or attributes according to preference.

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Nonparametric Tests - When Should the Marketer Use Them?

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