Use this Margin of Error calculator to measure the precision of your survey results and calculate sampling error. By calculating your margin of error (also known as a confidence interval), you can see how closely your results reflect the full population and how reliable your findings are.

The number of respondents who take your survey is usually just a sample of the total population. As an example, you can select at random 10 out of 50 employees from a department at your job. Those 10 are the sample and the 50 are the population.

Your margin of error is a range that your sample survey data is accurate when compared to the population.

As an example, let's say you were trying to decide between color scheme A and color scheme B for a new version of your company’s website. Your userbase is 200,000 total people. If you surveyed 600 users (your "sample size"), and 70% of them liked color scheme A could you rely on those survey results?

Using our margin of error calculator with a confidence level of 99% (meaning there's a 99% chance that your sample correctly reflects the opinions of your user base), you’ll see that the margin of error is 5%. That means a 99% likelihood that between 65% and 75% of your userbase will prefer color scheme A.

Confidence Level: A measure of how confident you are that your sample accurately reflects the population. Common standards used by researchers are 90%, 95%, and 99%.

Sample Size: The number of completed responses your survey receives is your sample size. It's called a sample because it represents a part of the total group of people whose opinions or behavior you care about. As an example, you can select at random 10 out of 50 employees from a department at your job. Those 10 are the sample and the 50 are the population.

The bigger the population is, the bigger the sample will need to be to accurately reflect the population. See our sample size calculator for how to calculate your needed sample size.

Population Proportion: This can be described as the makeup of the population. For example, if it's well known 60% of college students are female you could say the population proportion of college students is 60% female. If you wanted to mainly get opinions of college females, you would use this 60 percent in the formula below (for P). Often these numbers are not known and 50% (.50) is used for P. This .5 number produces the largest possible sample size, as it is the most conservative estimate.

Population Size:This is the size of your total population. Often this will be an extremely large number (such as the number of people in the United States). If you do not know your population size, it will be assumed that is infinitely large, and margin of error will be calculated using the first equation blow. If you do know your population size, such all the employees at your workplace, the margin of error will be calculated using the second equation below.

Margin of Error Equation (Infinity large population):

Margin of Error Equation (Finite population):

The z-score is the number of standard deviations a given proportion is away from the mean. To find the right z-score to use, refer to the table below:

Desired Confidence Interval	Z-score
80%	1.28
85%	1.44
90%	1.65
95%	1.96
99%	2.58

The margin of error helps determine how much confidence you can place in the reported results. Margin of error applies not only to surveys, but to any operational or analytical context where decisions rely on sampled data instead of full populations.

When testing messages, positioning statements, or concepts, margin of error is used to determine whether one option meaningfully outperforms another or whether observed differences fall within expected sampling variation. Clearly defined research questions reduce ambiguity and response noise, producing more consistent data. When combined with a sufficiently low margin of error, this allows differences in results to be interpreted with confidence and supports making informed changes to messaging.

Training feedback is often collected from a subset of participants rather than every attendee. Margin of error helps determine whether changes in satisfaction, comprehension, or effectiveness reflect meaningful trends or normal variation due to sample size, differing class sizes, or response frequency. When using a training evaluation form, margin of error provides context for interpreting score changes across sessions or teams, helping organizations focus on broader patterns rather than reacting to small, inconsistent swings while still identifying declines that warrant action.

Margin of error isn’t limited to surveys. In manufacturing, operations, and quality control, decisions are often based on sampled data rather than full inspection, such as estimating defect rates, evaluating process changes, or monitoring equipment performance. Manufacturing software development services often account for margin of error when building custom modules for operational monitoring, feedback data management, and KPI tracking, helping teams interpret sampled data reliably and avoid acting on misleading signals.