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In our last blog post we talked about the various types of sampling methodologies commonly used in market research. This blog post will explore how sample sizes are determined and calculating for sampling errors.

Each project is unique and bigger isn’t always better. While including more people in a sample population can yield more accurate results, the costs are often much higher. There are four values that need to be defined before determining the appropriate sample size for your study:

  • Population Size—How many people fit your demographic? Population size tells you WHO you need to survey to glean the types of insights you are going after for market research. For instance, if you are doing a market study to gauge interest in the roll-out of a new children’s clothing line in North America, you’d want to know approximately how many children live in North America who fit the parameters of the clothing sizes.
  • Margin of Error—Also known as the confidence interval, this number tells you how much lower or higher you’re willing to let your sample mean fall. You’ll likely recognize this during campaign season, when loads of polls and surveys are taken and reported with a margin of error or +/- x%.
  • Confidence Level—This percentage tells you how confident you can be that the actual mean falls within your confidence interval, or margin of error. The most common confidence intervals range between 90-99%. Confidence levels have a corresponding Z-score, which is a constant value needed when determining this equation. A 90% confidence level has a Z score of 1.645, a 95% confidence level has a Z score of 1.96, and a 99% confidence level has a Z score of 2.576.
  • Standard of Deviation—When setting up a sample population to survey, a common number to use is .5 as it often ensures that your sample will be large enough.


With the above values defined, it is now possible to calculate the needed sample size with the following formula: Sample Size= (Z-score)² * StdDev*(1-StdDev) / (margin of error)².


If after plugging in your numbers you find that the suggested sample size is too large, you can decrease your confidence level or increase your margin of error. Ultimately, the goal is to get a sample population that accurately reflects your target demographic so that qualitative research can be conducted.

InterQ prides itself on its proven track record working with companies of all sizes to conduct qualitative AND quantitative research.

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