For Accurate Survey Findings, Use Our Sample Size Calculator

Use the calculator below to find out how many people you'll need to survey to forecast how your customers or employees feel and think.

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How to Calculate the Perfect Sample Size

Enter the following information into the interactive calculator and discover exactly how many people to sample for your next customer survey.

  1. Set your population size
  2. Select your confidence level
  3. Choose your margin of error
  4. Receive your ideal sample size!

How Does Sample Size Change as Population Size Grows?

As your population size grows very large, the sample size doesn’t increase much—it actually flattens out. For example, even for a population of 1,000,000, you typically need fewer than 400 responses to get statistically valid results with a 5% margin of error.
However, for smaller populations, the sample size increases more quickly because you need a larger percentage of the population to get reliable data.

As an example, think about taking a poll in a small classroom versus a packed stadium. In the classroom, you might need to ask nearly everyone to get a clear picture of what the group thinks. But in the stadium, you can get a reliable sense of the crowd’s opinion by asking just a few hundred people.

a graph showing how many survey responses are needed based on the number of customers you have.

Get Your Sample Size Calculation In Seconds

Our Sample Size Calculator is built to help you back your decisions with data you can trust. This easy-to-use calculator makes finding the right survey sample size simple.

An employee uses the sample siza calculator to tell if she chose a large enough sample size

Why Sampling Matters

Your staff is more likely to get on board with your recommendations if your data represents your customer base proportionately.

Think of it this way: If you were running a large health study, you’d make sure your sample included multiple ethnicities and health profiles. Similarly, your data should represent the entirety of your customer base, not just those with free time, a gripe, or who provided their email.

An employee uses the sample size calculator to determine how many subjects she needs to sample from a finite population.

Get a Free Consulting Session ➔

Martha Brooke, CCXP & Six Sigma Black Belt, recommends the best methods for your objectives.

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Sample Size Calculator FAQs

What is the Minimum Survey Sample Size Needed to Collect Valid Data?

The answer depends on three factors: population size, confidence level, and the acceptable margin of error. Use the sample calculator above to determine how many subjects you need to sample based on your population size.


How Does Choosing the Right Sample Size Improve the Quality of Survey Data?

With the right sample size, you can collect responses that accurately represent the entire population of your survey. Calculating the right sample size reduces errors, increases reliability, and provides clear, accurate insights you can use to make data-backed decisions.


Does Sample Size Determine Whether or Not My Survey Provides Statistically Significant Results?

Yes. A larger sample size reduces the margin of error and increases the reliability of your results, which makes it more likely that differences or patterns are statistically significant and not due to random chance.

But there are limits.

While it’s true that a larger sample size reduces the margin of error, it also requires a larger investment of time, energy, and budget to collect data from everyone in your sample. Choosing an appropriate sample size is the best way to collect valid data without overextending your resources.


What is the Formula Used to Manually Calculate Sample Size?

The image below shows the sample size formula:

A formula for finding the sample size

Where:

This formula gives you the minimum sample size required to ensure your sample estimate is accurate and representative of the population. However, for CX teams, performing a manual sample size estimation can be time-consuming and prone to error. To save time and headaches, we recommend using our sample size calculator instead.


What is “Statistical Power?”

Statistical power is the likelihood that a test will reveal a difference if one truly exists. It measures the ability of a survey or experiment to avoid a Type II error, which occurs when you fail to identify a real effect.

To put it simply, with enough statistical power, your test is more likely to identify meaningful differences or relationships within your data.


Should I Sample Subgroups of My Target Population Separately?

Yes, you should sample subgroups of the same population separately to ensure proportional representation. This helps your survey reflect the diversity of the overall population, which makes the results more accurate.

For instance, if certain sub-populations (like age groups) make up different proportions of your audience, sampling them proportionately helps you capture their unique perspectives fairly without over- or under-representing one subgroup over another.


What Terms Should I Understand Before I Use the Sample Size Calculator?

Below is a brief glossary of terms related to the sample size calculator. It’s not exhaustive, but it covers many key terms related to statistics.

Random Sample: A random sample ensures that every individual in your target population has an equal chance of being selected. This reduces bias and improves the reliability of your results.

Margin of Error: The lower your margin of error, the more accurate your results will be. A 5% margin of error is often used in polling studies, and it’s generally the norm. Just keep in mind that a 5% margin of error is a plus or minus figure. That means your margin of error is actually a 10% spread, because you could be plus OR minus 5%.

Confidence Level: Statisticians often refer to a confidence level, and here is a simplistic explanation. Let’s assume you set your confidence level to 95%. If you were to repeat your experiment over and over again, the results would match the results from the actual population 95% of the time, or 19 times out of 20.

When you combine your margin of error (for example, 5%) and your confidence level (for example, 95%), you can say that you are 95% sure that the true measurement of the general population is within 5% of your survey results.

Confidence Interval: This is the range within which the true value of your survey results is likely to fall. For example, if your survey shows 60% satisfaction with a confidence interval of ±5%, the true satisfaction rate is likely between 55% and 65%.

Confidence Level Z-Score: The Z-score corresponds to your chosen confidence level. For example, a 95% confidence level has a Z-score of 1.96. It’s a statistical value that helps calculate the margin of error and sample size.

Standard Deviation: This measures the variability or spread of responses in your data. A larger standard deviation means responses vary widely, while a smaller one indicates more consistency.

Get a Free Consulting Session ➔

Martha Brooke, CCXP & Six Sigma Black Belt, recommends the best methods for your objectives.

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