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Kolmogorov-Smirnov (KS) Test Definition

Test of the goodness of fit between the observed distribution and the expected distribution using ordinal data.

The Kolmogorov-Smirnov (KS) test is a statistical method used in marketing research to assess the similarity of two probability distributions or to compare a sample distribution against a known distribution. In marketing research, it tests the goodness of fit between the observation distribution and  the professionals in the food and beverage industry, product development, or marketing. The method quantifies the maximum vertical distance between the cumulative distribution functions of the two datasets, thus providing insights into whether they originate from the same underlying distribution. The test is often used to analyze the fit between observed and expected data distributions. It doesn't require assumptions about the underlying distribution, making it versatile. Its importance lies in its potential to reveal discrepancies between observed and expected data, detect trends, identify outliers and validate the appropriateness of certain statistical models.

Who relies on the Kolmogorov-Smirnov (KS) Test?

Marketing researchers, data analysts and statisticians use the Kolmogorov-Smirnov (KS) Test to capture insights for making informed decisions about the distributional characteristics of data and whether certain assumptions hold true.

Why should I care about the Kolmogorov-Smirnov (KS) Test?

In marketing research, the Kolmogorov-Smirnov (KS) Test offers a rigorous and quantitative way to assess the goodness-of-fit between different datasets or between observed data and theoretical distributions. Test results can determine if the data being analyzed follows an expected pattern or if there are significant deviations that require further investigation. This aids in making accurate decisions and developing effective marketing strategies.