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Sample size for 1,000 people

A customer base, an SME's workforce, a neighborhood: when the total population is just 1,000 people, the '385 respondents' rule no longer applies as is. The finite population correction (FPC) kicks in fully: 278 respondents are enough for ±5% at 95% confidence — 28% of your target, but 28% fewer than the 'infinite population' sample.

The calculator below is prefilled with N = 1,000. One caveat specific to small populations: response rate becomes the real bottleneck. To collect 278 usable questionnaires at 40% participation, you must invite ~695 people — 70% of your base. Plan reminders from the design stage.

Confidence level

95% is the market research standard. Z-scores: 1.645 · 1.96 · 2.576 (NIST statistical tables).

The acceptable gap between your sample and reality. ±5% is the most common choice.

If unsure, leave 50%: it's the worst case, requiring the largest sample.

The total number of people in your target. Above ~100,000 the impact is negligible: leave empty.

Respondents needed

278

You need 278 respondents for a 95% confidence level with a ±5% margin of error.

Export:

How many respondents per precision level?

Precision is expensive: going from ±5% to ±2% multiplies the sample by 6.

101001,0001%3%5%8%10%Margin of error

Summary table

Sample size for the most common combinations.

Summary table
Confidence± 3%± 5%± 10%
90%43021464
95%51727888
99%649400143

Sample size: done. Now, the fieldwork…

Traditional fieldwork takes 6 weeks and $10,000. Panelia simulates 300+ calibrated respondents in 10 minutes.

Simulate my study

Frequently asked questions

Why fewer respondents than with a large population?
Because sampling 278 people out of 1,000 exhausts a significant share of the possible variability: the correction n = n₀/(1+(n₀−1)/N) translates that information gain mathematically.
What if I survey all 1,000 people?
That's a census: no sampling error at all. Often relevant internally (employee surveys) when the cost per answer is low — the real issue becomes response rate and non-response bias.
278 out of 1,000 is a big sampling rate. Is that a problem?
No — quite the opposite: that's precisely what the FPC exploits. The challenge is operational: reaching 28% participation requires reminders and a short questionnaire.
My base is 800 or 1,500 people, not 1,000?
Enter the exact value in the 'population size' field: the calculation adjusts instantly. For 800 people you need 260 respondents; for 1,500, 306 (at 95%, ±5%).