Frequency Distributions in Marketing Research

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  • 0:04 What Is a Frequency…
  • 1:23 Weird Data
  • 2:42 What To Do with Your Data
  • 3:29 Too Much Information
  • 3:51 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.

The foundation of statistical research in marketing is the frequency distribution. This lesson explores why a frequency distribution is vital for visualizing, sorting, and analyzing the findings from marketing research.

What Is a Frequency Distribution?

Recently, the Gallup organization was contracted to conduct research regarding church attendance across the United States. The survey was commissioned to find out which states had below, above, or average church attendance. In terms of market research, questions don't get much more straightforward than this.

The required calculations are pretty simple, and the presentation of the results is easily done using a chart that depicts the frequency distribution, which is an organized visual display - whether it's a table, chart, or graph - that shows frequency counts. It's the most fundamental way to report research results because it allows consumers of the data to visualize, sort, and analyze the data quickly and effectively. It's the first step in analyzing survey data. In the study we just mentioned, a chart depicting the frequency distribution might look something like this:

Weekly Church Attendance Chart

If this study were to be used for market research purposes, entities that might find this data helpful would include businesses like a Christian bookstore or an audio-visual equipment vendor. In addition, some businesses could use this market research to make a decision not to offer their products in the area. These organizations might be liquor stores, casinos, adult bookstores, and/or gentleman's clubs.

Weird Data

Have you ever heard a statistic and thought to yourself, 'How could that possibly be accurate?' That's a great question, and more often than not, you're right. In any frequency distribution, there are data values known as outliers, data elements which occur on the extreme ends of the frequency distribution. In many cases, outliers must be removed before any calculations occur because a single value can skew the entire distribution. However, a statistician cannot discard extreme values simply because they're extreme. Take a look at this frequency distribution chart and see if you can identify if there are any outliers, and then determine if the data should be removed from the distribution:

Chart A Outlier Present

Chart B

Take a look at the values for grader rentals in July. The value (920) is clearly invalid, data that is inaccurate or incorrect. Invalid, or erroneous, data clearly indicates a data entry error or measurement error. For this distribution, the value should be discarded because it's invalid. Removing outliers is part of the data cleaning, a process that removes all invalid values from a distribution prior to analysis. Now take a look at the same chart that has corrected the invalid data. Notice the huge change in the line graph? July no longer looks like a witch's hat!

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