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How many responses do you need?
Use your population size, confidence level, and margin of error to estimate a recommended survey response sample.
Recommended sample size
383
Population: 100,000 • Confidence: 95% (Z = 1.96) • Margin of error: 5%
You need approximately 383 completed responses.
This estimate assumes a simple random sample and a 50/50 response split for maximum variability.
Have your sample size. Now you need respondents. Explore Corvane Audience.In survey research, sample size is the number of completed responses you need before you can draw conclusions about a larger population. The population is everyone you want to understand; the sample is the group who actually answers. A well-chosen sample gives your study enough statistical confidence to be useful while respecting the time, budget, and attention of the people you invite. The right number depends on how precisely you want to estimate the population and how much uncertainty you can accept. That is where confidence level and margin of error come in. Confidence level describes how certain you want to be that the sample reflects the wider population, while margin of error expresses the possible range between your sample result and the true population value. Sample size does not guarantee representativeness on its own—sampling method, coverage, question quality, and response quality still matter—but it gives your survey a credible statistical foundation.
This survey sample size calculator begins with the standard large-population formula: n₀ = Z² × p(1 − p) / e². Z is the score associated with your confidence level, p is the expected proportion, and e is the margin of error as a decimal. We use p = 0.5 because it produces the most conservative required sample when the expected proportion is unknown. For a finite population, the calculator applies a finite population correction so the recommendation reflects the actual number of people available to survey. The final result is rounded up to the next complete response. This makes the method transparent without turning your planning process into a statistics exercise.
The calculator assumes a simple random sample. For complex designs, quotas, or weighted studies, consult a research statistician.
A 90% confidence level can be useful when a lower confidence threshold is acceptable and the research is directional. A 95% confidence level is the common default for many surveys, consumer research, and market research applications because it balances certainty with practicality. A 99% confidence level is useful when a higher degree of statistical confidence is required, though it also raises the required sample size. The right choice depends on the consequences of being wrong, the decisions at stake, and the context of your research.
A ±1% margin of error is very precise, but it requires a large sample. ±3% is suited to more precise research with a larger sample requirement. ±5% is a common benchmark for many surveys because it balances cost and precision, while ±10% can be appropriate for exploratory research where a smaller sample is acceptable. No single margin is universally correct—the appropriate threshold depends on how the results will be used and how much variation the decision can tolerate.
There is no universal survey response sample size. The table below uses a 95% confidence level and ±5% margin of error to show how required sample size changes as your population grows.
Population
Confidence
Margin of error
Recommended sample
1,000
95%
5%
278
10,000
95%
5%
370
100,000
95%
5%
383
1,000,000
95%
5%
384
For consumer research and market research, the right sample size depends on population size, desired precision, confidence level, research objectives, segmentation requirements, subgroup analysis, and expected response quality. A statistically sufficient total sample may not be sufficient for comparing demographic or behavioral segments. If you need to compare markets, age groups, purchase behaviors, or audience types, each subgroup needs enough completed responses to support the analysis. That often means recruiting beyond the calculator’s overall recommendation.
What is a good sample size for a survey?
It depends on population size, confidence level, margin of error, and the research objectives. A calculator helps you estimate a statistically appropriate starting point.
How many survey responses do I need?
There is no universal number. This calculator estimates the required sample based on the population, confidence level, and margin of error you select.
Is 100 respondents enough for a survey?
One hundred responses can be useful for exploratory research, but it often produces a larger margin of error than a larger sample and may not support subgroup analysis.
Is 200 respondents enough for a survey?
It can be useful depending on the population and research goal, but you should evaluate the desired confidence level, margin of error, and any segment comparisons you need to make.
What is the standard confidence level for surveys?
95% is a common choice for surveys, although it is not mandatory for every study. The suitable confidence level depends on the research context.
What is a 5% margin of error?
It means a survey estimate may be within five percentage points of the true population value, assuming the sample and study design meet the calculation’s assumptions.
Does a larger population always require a larger sample size?
Sample size rises with population size at first, but for very large populations the required sample changes very little when confidence level and margin of error stay the same.
How accurate is a sample size calculator?
It gives a statistical estimate based on stated assumptions, not a guarantee of sample quality. Sampling method, coverage, response quality, and survey design still matter.
What happens if my survey has a low response rate?
Plan to invite more people than your required completed-response sample. The number of invitations needed depends on the response rate you expect.
Once you know how many responses you need, Corvane Audience can help you reach the right people for your research.