Sample Size & Confidence Levels for Marketing Research Studies

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  • 0:03 What Is the Right Number?
  • 0:56 Deciding Sample Size
  • 2:52 Sampling Distribution
  • 3:34 Lesson Summary
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Lesson Transcript
Instructor: David Whitsett

David has taught computer applications, computer fundamentals, computer networking, and marketing at the college level. He has a MBA in marketing.

If you can't reach them all, how do you decide how many people to survey to get a representative sample of a population? In this lesson, we'll examine sample size considerations and how to maximize your confidence level in the data.

What Is the Right Number?

Say you have a chain of tire stores in the Southeast in every state except Florida. You're thinking about entering the Florida market, but you're not sure which cities to hit or even if it's a good idea at all. You pick Orlando to start because of its central location in the state, but there are roughly 2.3 million people in the Orlando metro area. If you want to survey folks in Orlando to see what they think about a new tire store, where do you start?

You probably can't survey everyone in Orlando, and you wouldn't want to anyway. Only adults buy tires, so you'd want to limit the survey to adults and exclude children. Let's say you did your research and the magic number was 1,276,000 adults (round numbers based on the size of the area workforce). That's still too many to survey all of them, so you need to determine the size of the sample (a group that has representative characteristics of the larger population).

Deciding Sample Size

Figuring out the correct sample size is a key decision—too many, and you've wasted resources; not enough, and the data might not be reliable. There are two measures you hear discussed in conjunction with sample size that affect the accuracy of the data.

  • The confidence interval, also known as the margin of error, is the plus or minus figure you hear reported in political polls on television news. For example, if you set a confidence interval of 5 and 56% of your sample picks answer B, you can be sure that if the whole population answered the same question, between 51% (minus 5) and 61% (plus 5) would also give answer B.

  • The confidence level tells you how certain you can be that someone in the larger population who answers a question would fall within the margin of error. For example, a 95% confidence level is an industry standard. So in the tire example, if you used a 95% confidence level, you're 95% sure that between 51% and 61% of the whole population would answer the question the same way as your sample.

So, in our example, the confidence interval is the range of values likely to give answer B, and the confidence level is the degree of certainty that those folks will choose answer B.

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