Parametric & Non-Parametric Tests in Marketing Research Video

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  • 0:03 Accurate Calculation
  • 1:01 A Rookie Mistake
  • 3:44 When Nothing but the…
  • 4:56 Lesson Summary
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Lesson Transcript
Instructor: Scott Tuning

Scott has been a faculty member in higher education for over 10 years. He holds an MBA in Management, an MA in counseling, and an M.Div. in Academic Biblical Studies.

Parametric and non-parametric tests are analytical techniques used to analyze statistical data with differing degrees of precision and reliability. This lesson will explore both tests and will provide tips for choosing the most appropriate test.

Accurate Calculation

Before market researchers begin a study, they have to make some important choices regarding their methods. After asking, refining, and finalizing the research question (what specifically do we want to know), the researcher will determine the best method for answering the research question. The choice of methodology is largely based the degree or precision and reliability required to answer the question.

When market researchers need to draw definitive conclusions based on their data, a parametric test is appropriate. Parametric tests are statistical calculations that produce high-quality, actionable data. Non-parametric tests are less precise but easier to facilitate. Before exploring these tests in detail, a brief recap of a normal distribution is in order, since a parametric test assumes a normal distribution, while a non-parametric test does not require a normal distribution.

A Rookie Mistake

An educational services company, XYZ Learning Resources, is a leading provider of certification review materials for students preparing for major licensing exams. XYZ is very aware that unless their products can be definitively proven to have a statistically significant improvement in pass rates, their company will fail. To that end, XYZ retains a market research firm to keep a close eye on the data that the company needs to be responsive to in order to stay in business. It is not likely that the market research firm would even consider a non-parametric test because the data they need is quantitative, and not qualitative.

It's Just a Simple Average, Right?

XYZ regularly hires interns who are completing their graduate studies in marketing, and one of the first tasks assigned to an intern is to calculate the average standardized test score for customers using the XYZ CPA study guide. The most recent intern performed her calculations and reported back to her supervisor that the average score was 67% - a score that would not be passing on the exam. But none of the other data seemed to indicate that there was regularly such a low score.

The intern approached her supervisor to ask for help determining what she did wrong. Her supervisor had fielded this question a number of times, and he was ready to discuss the importance of normal distributions and parametric tests.

Nope - Not Just a Simple Average

A normal distribution, sometimes called a bell curve, is a statistical function that adjusts data so that the middle value (the median) really does correspond to the middle value of the data. Likewise, a normal distribution adjusts data so that the average (the mean) does accurately depict the average of the data.

When the intern calculated the average exam score, she failed to recognize that the data contained several scores of ''0'' because the zero score was placed in the database when a registrant did not show up to take the exam. These zero values were erroneous outliers that needed to be removed from the data. This table shows the distribution of scores before the outliers were removed.

Table 1

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