Survey Sample Size Calculator
Calculate how many completed survey responses you need from your population for a chosen confidence level and margin of error.
Recommended completed responses
for a 95% confidence level and ±5% margin of error
How the sample size calculation works
The calculator estimates a sample for a population proportion, then applies a finite population correction. A 50/50 expected split is the conservative default because it produces the highest required sample size.
The result is the number of completed responses, not invitations. If you expect a 20% response rate and need 377 completes, plan to invite roughly 1,885 eligible people.
Choose realistic research settings
- Use 95% confidence and ±5% margin of error as a common planning starting point, then tighten the margin only when the decision justifies the extra responses.
- Use the addressable population for the decision, such as active customers in a market, not every visitor your store has ever had.
- If you will compare customer segments, each segment needs enough responses for the precision you want within that segment.
What this calculator does not prove
A large sample does not remove selection bias, non-response bias, leading questions, or poor timing. Statistical precision matters only after the people who respond reasonably represent the population you want to understand.
Put the result to work
UserLoop connects survey answers to Shopify order data so you can move from a one-off calculation to continuous customer insight.
Create your first surveyFrequently asked questions
What survey sample size do I need for 10,000 people?
At 95% confidence, a ±5% margin of error, and a conservative 50/50 split, the calculator returns 370 completed responses for a population of 10,000.
Why does population size stop making a big difference?
Once a population is much larger than the sample, the finite population correction becomes small. Precision is then driven mostly by the confidence level, margin of error, and expected response split.
Should I use 90%, 95%, or 99% confidence?
95% is a common research convention. A higher confidence level requires more responses; a lower level requires fewer. Choose based on the decision risk rather than treating one setting as universally correct.
Related resources
- Margin of error calculator
- Confidence interval calculator
- Survey methodology guides
- Response rate calculator
Sources and guidance
Reviewed by the UserLoop editorial team.