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Sample size at 99% confidence

The 99% confidence level is the choice for high-stakes decisions: regulatory studies, health, hard-to-reverse investments. The trade-off is direct: with z = 2.576, the required sample climbs about 73% above the 95% standard. For ±5%, you need 664 respondents; for ±1%, over 16,500.

Before paying that premium, ask the reverse question: does the error you're guarding against cost more than ~280 extra respondents? If your study feeds an exploration or a directional signal, 95% is plenty. The calculator below is preset to 99% to size your exact case.

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

664

You need 664 respondents for a 99% 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.

1001,00010,000100,0001%3%5%8%10%Margin of error

Summary table

Sample size for the most common combinations.

Summary table
Confidence± 3%± 5%± 10%
90%75227168
95%1,06838597
99%1,844664166

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

How many respondents at 99% confidence and ±5%?
664 respondents for a large population, versus 385 at 95%: moving to 99% increases the sample by about 73% at constant margin.
When is 99% truly warranted?
When the cost of a wrong conclusion far exceeds the cost of extra fieldwork: compliance, safety, major investment decisions. For a marketing concept test, it's usually oversized.
Why not 100% confidence?
Impossible with a sample: absolute certainty would require surveying the entire population. The z-score goes to infinity as confidence approaches 100% — and so does the required sample.
Does 99% confidence reduce the margin of error?
No, they're two independent dials. At equal sample size, REQUIRING 99% widens the reported interval. To keep ±5% while moving to 99%, you must grow the sample.