What’s a good sample size in market research?

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One of the most common questions we get asked by people doing surveys is “How big should my sample size be?”. While there are many sample size calculators and statistical guides available out there, those who never did statistics at university (or have forgotten it all) may find them intimidating or difficult to use.

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What Is Sample Size?

Sample size is an often-used term in statistics and market research, and one which inevitably comes up when you’re surveying a large population of respondents. It relates to the way in research is conducted in extremely large populations.

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So what exactly is sampling, and why does sample size actually matter?

When you survey a large pool of respondents, you’re interested in the entire group. However, it’s not realistically possible to get answers or results from categorically everyone. So you select a random sample of individuals which characterises the population as a whole.

The size of the sample is quite important for getting accurate, statistically significant results as well as running your study successfully. If your sample is too small, you could include a disproportionate number of individuals who are outliers as well as anomalies. These skew the outcomes and you don’t get a fair picture of the entire population.

If the sample is too big, the entire study becomes complex, expensive and time-consuming to run, and though the results are more accurate, the benefits don’t outweigh the costs.

What Is A Recommended Sample Size?

The sample size that is normally recommended for most market research studies is based upon the industry standard 95% confidence level, that has an accuracy rate of ± 5%.

The term ‘confidence level’ refers to the likelihood that the final results will not deviate by more than a particular percentage from the actual population statistics (i.e., the results obtained if everyone in the population were to be surveyed).

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Using The 95% Confidence Table

The level of reliability of the final data can be determined. Keeping in mind the desired goal of ± 5%, when conducting a study on a population size of 100 000 it would be necessary to conduct 400 surveys. In this case, the reliability of the results would be ± 4.9% at the 95% confidence level. This has the result that in 95 out of 100 repetitions of the survey, the results will not vary more than ± 4.9%.

It’s a dirty little secret among statisticians and market researchers that sample size formulas frequently require you to have information in advance which you don’t normally have. For instance, you typically are required to know (in numerical terms) how much the answers in the survey will probably vary between individuals (if you knew that in advance then you wouldn’t be doing a survey!).

So even though it’s theoretically possible to calculate a sample size utilising a formula, in a number of cases experts still end up relying on rules of thumb plus a good deal of common sense and pragmatism. That means you shouldn’t worry too much if you can’t use fancy maths to choose your sample size – you’re in good company.

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