Sample Size Calculator
How many survey responses do you need? Cochran's formula with finite population correction.
Free · No signup · By the analysts at Blackridge Research · Updated 2026-07-18
Inputs
Results
What this means
Adjust the inputs to calculate.
About the Sample Size Calculator
Sample size is the number of responses you need from your target population to achieve a given level of statistical precision.
Cochran's formula is the standard method, with finite population correction when you're sampling a significant fraction of the total population.
Formula
n₀ = (z² × p × (1-p)) / e² | n = n₀ / (1 + (n₀-1)/N)
- n
- — Required sample size (with finite population correction)
- z
- — Z-score for confidence level (1.645 for 90%, 1.96 for 95%, 2.576 for 99%)
- p
- — Expected proportion (use 0.5 for maximum sample size)
- e
- — Margin of error (as decimal)
- N
- — Total population size (for finite population correction)
How to use this calculator
- 1
Enter your population size
The total number of people in your target population.
- 2
Choose your confidence level
Common choices: 90%, 95%, or 99%.
- 3
Set your margin of error
How much precision do you need? Common choices: 3%, 5%, or 10%.
- 4
Enter expected proportion
Use 50% for maximum sample size, or your best estimate.
Example calculations
B2B buyer survey
You need to survey logistics decision-makers from 42,000 companies with 95% confidence and 5% margin of error.
n₀ = 384. With finite population correction: n = 384 / (1 + (384-1)/42000) = 380 responses needed.
- Required sample size:
- 380
- Outreach needed (30% response):
- 1,267
- Response rate assumption:
- 30%
You need 380 responses. At a 30% response rate, reach out to 1,267 people.
Interpreting your results
The required sample size is the minimum number of responses you need for your results to be statistically valid.
If your population is small (under 10,000), the finite population correction significantly reduces the required sample size.
A higher confidence level or lower margin of error increases the required sample size.
Need the market data behind this calculator?
The Sample Size Calculator is only as good as its inputs. Blackridge Research publishes syndicated market reports with vetted market sizes, growth rates, and competitive landscapes across 40+ industries — and builds custom studies when the shelf report doesn't exist.
Industry applications
Business
- Market research: plan surveys for customer satisfaction, product feedback, and market sizing.
- Employee engagement: design employee surveys with statistically valid samples.
- Product development: gather user feedback with confidence in the results.
Construction
- Safety surveys: plan statistically valid safety culture assessments.
- Quality control: determine inspection sample sizes for construction materials.
- Customer satisfaction: survey project owners and contractors.
Research
- Academic research: calculate required sample sizes for research studies.
- Market studies: plan quantitative research with appropriate sample sizes.
- Feasibility studies: determine survey requirements for feasibility assessments.
Common mistakes to avoid
-
✗ Forgetting the finite population correction
When sampling more than 5% of a small population, you must apply the correction.
-
✗ Using 50% when you know the proportion
Use your best estimate for expected proportion to reduce sample size.
-
✗ Confusing margin of error with confidence level
They are different: margin of error is precision; confidence level is certainty.
Frequently asked questions
›What is a good sample size?
It depends on your population and desired precision. For large populations, 384 is the minimum for 95% confidence at 5% margin of error.
›When should I use finite population correction?
When your sample exceeds 5% of the total population, typically for populations under 10,000.
Glossary
- Sample size:
- The number of responses needed for statistical validity.
- Cochran's formula:
- The standard formula for calculating sample size.
- Finite population correction:
- Adjustment for sampling a significant fraction of a small population.
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