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Survey Sample Size Calculator

Calculate required sample size for a survey.

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Results

Sample Size

Required responses

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Assumptions

  • Margin of error should be a positive percentage and distribution should be between 0% and 100%.
  • The calculator uses Cochran's formula with a finite population correction when a population size is supplied.
  • Using a 50% distribution gives the most conservative sample-size estimate when the true proportion is unknown.

Sensitivity preview

Margin of Error -10%: Sample Size 475

Margin of Error Current: Sample Size 385

Margin of Error +10%: Sample Size 318

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About this Sample Size

Before conducting a survey, you need to know how many responses are required to make the results statistically significant. Surveying too few people leads to unreliable data; surveying too many wastes resources. This calculator helps you find the sweet spot.

Who this calculator is for

  • Survey designers, product teams, and researchers planning how many responses they need.
  • Students learning how confidence level and margin of error affect study design.
  • Anyone estimating sample requirements before running a poll, audit, or experiment.

When to use it

  • Use it before data collection to set a realistic response target.
  • Use it to see how tighter margins of error or higher confidence levels increase sample requirements.
  • Use it alongside confidence interval planning when you want more precise estimates.

How to use this calculator

  1. Select your Confidence Level (95% is the industry standard).
  2. Enter your desired Margin of Error (usually 5%).
  3. Enter the Population Size (leave as 0 if the population is very large or unknown).
  4. The result is the number of completed surveys needed.

Key assumptions

  • Margin of error should be a positive percentage and distribution should be between 0% and 100%.
  • The calculator uses Cochran's formula with a finite population correction when a population size is supplied.
  • Using a 50% distribution gives the most conservative sample-size estimate when the true proportion is unknown.

Accuracy and testing

Review formula sources, default test cases, assumptions, unit handling, rounding rules, and changelog for this calculator.

View accuracy page

How this calculator works

The calculator uses Cochran's Formula for infinite populations, adjusted for finite populations if a size is provided. n = (Z² × p(1-p)) / e² * Z: Z-score for confidence level (e.g., 1.96 for 95%) * p: Population proportion (assumed 50% for maximum sample size) * e: Margin of error (decimal)

Inputs explained

Confidence Level

How sure you want to be that the actual result lies within your margin of error. 95% is standard for most research.

Margin of Error

The range of error you are willing to accept (e.g., +/- 5%). Smaller error requires a much larger sample.

Population

The total group size you are studying. If > 20,000, the sample size doesn't change much, so you can leave it blank.

Understanding your results

The result is the number of completed responses you need. If you expect a 10% response rate to your email survey, you would need to send invitations to 10x this number of people.

Example scenarios

Political Poll

Confidence 95%, Error 3%, Population Infinite. Sample Size = 1,068.

Employee Survey

Confidence 95%, Error 5%, Population 500 employees. Sample Size = 218.

Key Features

  • Finite Population Correction
  • Standard Confidence Levels
  • Conservative Distribution (50%)

Tips & Best Practices

  • If you don't know the population proportion (distribution), use 50%. This gives the most conservative (largest) sample size.
  • Reducing the margin of error from 5% to 1% increases the required sample size by 25 times.

Glossary of Terms

Population
The entire group of people you want to draw conclusions about.
Sample
The specific group of individuals that you will collect data from.
Margin of Error
A percentage that describes how closely the answer your sample gave is to the 'true value' in the population.

Frequently Asked Questions

Why is 50% distribution used?
A response distribution of 50% (e.g., 50% yes / 50% no) requires the largest sample size. Using this assumption ensures your sample is large enough regardless of the actual outcome.
What is the difference between 95% and 99% confidence?
At 95% confidence, there is a 5% chance your results are due to random luck. At 99%, there is only a 1% chance. Higher confidence requires a larger sample size.
Does population size matter?
Only for small populations. Once the population exceeds ~20,000, the math for an 'infinite' population applies, and the required sample size stops growing significantly.