How many people do you need to interview to get results representative of the target population with the level of confidence that you are willing to accept?


Qualtrics offers a sample-size calculator that can help you determine your ideal sample size in seconds. Just put in the confidence level, population size, margin of error, and the perfect sample size is calculated for you.

To learn more about the variables you can read this post on how to calculate sample size or scroll down to learn more about confidence interval or level.

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The confidence interval is the plus-or-minus figure that represents the accuracy of the reported. Consider the following example:

A Canadian national sample showed “Who Canadians spend their money on for Mother’s Day.” Eighty-two percent of Canadians expect to buy gifts for their mom, compared to 20 percent for their wife and 15 percent for their mother-in-law. In terms of spending, Canadians expect to spend $93 on their wife this Mother’s Day versus $58 on their mother. The national findings are accurate, plus or minus 2.75 percent, 19 times out of 20.

For example, if you use a confidence interval of 2.75 and 82% percent of your sample indicates they will “buy a gift for mom” you can be “confident (95% or 99%)” that if you had asked the question to ALL CANADIANS, somewhere between 79.25% (82%-2.75%) and 84.75% (82%+2.75%) would have picked that answer.


The confidence level tells you how confident you are of this result. It is expressed as a percentage of times that different samples (if repeated samples were drawn) would produce this result. The 95% confidence level means that 19 times out of twenty that results would fall in this – + interval confidence interval. The 95% confidence level is the most commonly used.

When you put the confidence level and the confidence interval together, you can say that you are 95% (19 out of 20) sure that the true percentage of the population that will “buy a gift for mom” is between 79.25% and 84.75%.

Wider confidence intervals increase the certainty that the true answer is within the range specified. These wider confidence intervals come from smaller sample sizes. When the costs of an error is extremely high (a multi-million dollar decision is at stake) the confidence interval should be kept small. This can be done by increasing the sample size.

This post was originally published in 2010 and updated in May 2018.