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Reducing Sampling & Non-Sampling Errors in Marketing Research

Reducing Sampling & Non-Sampling Errors in Marketing Research
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  • 0:04 Common Issues in…
  • 0:28 Sampling & Non-Sampling Errors
  • 1:19 Minimizing Sampling Error
  • 2:26 Minimizing Non-Sampling Error
  • 4:30 Lesson Summary
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Lesson Transcript
Instructor: Danielle Reed

Danielle works in digital marketing and advertising. She holds a bachelor's degree in English and an MBA.

In this lesson, we cover the most common types of errors in marketing research: sampling and non-sampling errors. Then we discuss techniques for reducing marketing research errors.

Common Issues in Marketing Research

Have you ever filled out a survey or questionnaire, or better yet, have you ever written one? Often, the most basic form of market research is difficult to write in a manner that is unbiased, fair, and thorough. After writing the content, how do you get a sample population that is representative of the whole? Marketing research is not easy.

Sampling & Non-Sampling Errors

There are two major types of errors in marketing research: sampling error and non-sampling error. Sampling error occurs when the sample used in the study is not representative of the whole population. Non-sampling error encompasses all types of errors, mostly caused by human error, such as questionnaire wording, data entry errors, and biased decisions.

Error occurs so often that it is a statistical practice to include a margin of error in final results. A margin of error is the amount allowed for in case of a miscalculation to represent the difference between the sample and the actual population. As a person reading a study, you will see something like, 'The margin of error for this data is plus or minus 3%.'

Minimizing Sampling Error

Of the two types of errors, sampling error is easier to identify. The biggest techniques for reducing sampling error are:

Increase the sample size.

A larger sample size leads to a more precise result because the study gets closer to the actual population size.

Divide the population into groups.

Instead of a random sample, test groups according to their size in the population. For example, if people of a certain demographic make up 35% of the population, make sure 35% of the study is made up of this variable.

Know your population.

The error of population specification is when a research team selects an inappropriate population to obtain data. Know who buys your product, uses it, works with you, and so forth. With basic socio-economic information, it is possible to reach a consistent sample of the population. In cases like marketing research, studies often relate to one specific population like Facebook users, Baby Boomers, or even homeowners.

Minimizing Non-Sampling Error

Non-sampling error is broad because the types of marketing studies conducted are various. The following are general techniques used to minimize non-sampling error, but remember, an in-person study has different factors than a survey or questionnaire.

Randomize selection to eliminate bias.

Select participants based on a random factor, like choosing every fourth person on a list.

Train your team.

If the study is conducted by a researcher, either use the same researcher or be sure to train your team on the procedure. Training and experience is essential.

Perform an external record check.

Human error occurs in entering data. Have an external source check your records and confirm their consistency with written results. Entering the number 20 as the number 200 is an easy mistake that could throw off your research dramatically.

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