Free Sample Size Calculator

Find out how many people you need to survey for a result you can trust. Set your confidence level, margin of error, population size, and response distribution, and get the required sample size the moment you change a value. It uses the standard statistical formula, and every calculation runs locally in your browser, so nothing you enter is ever sent to a server. Free, no sign-up.

A sample size calculator tells you the minimum number of completed responses you need for a survey result to be statistically reliable, based on your confidence level, margin of error, population size, and expected response distribution. This free tool applies the standard formula for a proportion and shows the number instantly, with no sign-up, and it runs entirely in your browser so nothing you enter leaves your device.

Your parameters

Confidence level

How sure you want to be that the true answer falls inside your margin of error. 95% is the common default.

Margin of error (%)

How much error you will accept, in percentage points. A smaller margin means a larger sample.

Population size (optional)

The total number of people you are studying. Leave it blank for a large or unknown population.

Response distribution (%)

Your best guess at how a yes/no answer will split. Leave it at 50% when you are unsure: that gives the most conservative, largest sample.

Required sample size

385

responses needed

To be 95% confident within ±5%, survey about 385 people from a large or unknown population.

What these mean

Confidence level

How often the true value would land inside your margin of error if you repeated the survey. At 95%, you would be right 19 times out of 20.

Margin of error

The band around your result, in percentage points. A 5% margin on a 60% finding means the real figure sits somewhere between 55% and 65%.

Response distribution

Your expected split on a yes/no question. 50% is the safest assumption because it requires the most responses, so you never undersample.

This is a statistical estimate for a simple random sample. Real accuracy also depends on response quality, sampling method, and how representative your respondents are.

How the sample size calculator works

Three steps, and it runs in your browser.

Set your parameters

Pick your confidence level and margin of error, add your population size if it is small, and set the response distribution (leave it at 50% if you are unsure).

Read the sample size

The tool applies the standard sample-size formula for a proportion and shows the number of responses you need, updating the moment you change any value.

Copy and plan

Copy the summary and use the number to plan how many people to invite. Everything is calculated locally in your browser and never sent to any server.

Free, private, and built for better research

Most sample-size calculators want your email first, and many run the numbers on someone else's server. This one does neither. There is no AI model and no API call behind it: the formula is fixed, documented, and the arithmetic happens on your machine.

100% in your browser

Nothing you enter is sent to a server. The calculation is plain arithmetic that runs on your device, so your research plans stay private.

No account required

No sign-up, no email wall, no record of what you calculated.

Arithmetic you can check

The same inputs always produce the same number. You can read the formula below and verify it yourself.

The formula this tool uses

The standard sample-size formula for estimating a proportion, so you can sanity-check any result:

Z is the z-score for your confidence level: 1.645 for 90%, 1.96 for 95%, 2.576 for 99%.
p is the response distribution as a proportion, and e is the margin of error as a proportion.
The large-population sample size is n0 = (Z squared times p times (1 minus p)) divided by e squared.
If you enter a population size N, the finite population correction gives n = n0 divided by (1 plus (n0 minus 1) divided by N).
The result is rounded up to a whole number of people, because you cannot survey a fraction of a person.

Where this formula comes from

It is the textbook method for sampling a proportion, not something invented for this tool:

Sample size determination: the standard formula for estimating a proportion and the finite population correction.
Confidence intervals: the basis for the z-scores used at each confidence level.

Built for every team that runs surveys

Anyone who has argued about whether a result is "enough people" knows the answer is a number, not an opinion. This tool gives you that number before you field the survey. It pairs well with an adaptive AI survey platform when the answers you collect are sensitive.

Researchers

Size your sample before fieldwork and state it in the method section with confidence.

Product teams

Know how many users you need before a feature survey is worth trusting.

Marketing & CX

Back a brand or satisfaction study with a defensible sample rather than a round number.

HR & people teams

Work out how many employees make an engagement result representative.

Academics & students

Calculate the sample your ethics submission or dissertation asks you to justify.

Agencies

Show a client exactly what a tighter margin of error costs in respondents.

Know your sample size? Now collect it privately.

BlockSurvey is an AI survey platform, encrypted end to end, so the responses you gather are never sold, mined, or used to train models.

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Frequently asked questions

What is a sample size calculator and what does it do?

It tells you how many completed responses you need for a survey result to be statistically reliable. You choose how confident you want to be (the confidence level), how much error you are willing to accept (the margin of error), and, optionally, how large the group you are studying is (the population size). The tool returns the minimum number of people you need to survey. It is a deterministic statistical calculation, not an AI model, so the same inputs always produce the same number.

How is the sample size calculated?

It uses the standard formula for a proportion. First it computes the sample size for a very large population: n0 = (Z squared times p times (1 minus p)) divided by e squared, where Z is the z-score for your confidence level, p is the response distribution as a proportion, and e is the margin of error as a proportion. If you enter a population size N, it applies the finite population correction: n = n0 divided by (1 plus (n0 minus 1) divided by N). The result is rounded up to a whole number of people.

What happens to the numbers I enter?

Nothing is sent anywhere. This tool makes no API calls and does no AI processing: the calculation runs entirely in your browser using plain arithmetic, and none of your inputs ever reach a server. That is why there is no sign-up and no waiting.

What confidence level and margin of error should I use?

A 95% confidence level with a 5% margin of error is the common default for most surveys and market research. Raise the confidence level to 99% or tighten the margin to 3% or 2% when a decision carries real cost and you need more certainty, but be ready for the required sample size to climb sharply. Lower confidence or a wider margin reduces the sample you need, at the cost of precision.

What is the response distribution and why does it default to 50%?

The response distribution is your best guess at how the answers will split for a yes or no question. A 50/50 split is the most conservative assumption because it produces the largest required sample size, so if you have no prior data, leave it at 50% and you will never undersample. If you already know an answer is likely to be lopsided, say 80/20, entering that lowers the sample size you need.

Do I need the population size?

Only if your population is small. Leave the field blank for a large or unknown population and the tool uses the standard large-population formula. When the population is limited, for example the 400 employees at a company or the 900 customers on a list, entering it applies the finite population correction, which lowers the number of responses you actually need. For populations in the tens of thousands or more, the correction makes almost no difference.