Syntax
CONFIDENCE.NORM(alpha, standard_dev, size)
| Parameter | Description |
|---|---|
| alpha | Parameter of the CONFIDENCE.NORM function. |
| standard_dev | Parameter of the CONFIDENCE.NORM function. |
| size | Parameter of the CONFIDENCE.NORM function. |
Examples
Manufacturing tolerance
Formula
=CONFIDENCE.NORM(0.05, 0.3, 100)
Returns 0.059 — with 100 samples and known std dev of 0.3, the true mean is within 0.059 units of the sample mean (95% confidence).
Survey margin of error
Formula
=CONFIDENCE.NORM(0.05, 15, 1000)
Returns 0.93 — a poll of 1000 people with std dev 15 has a margin of error under 1 point at 95% confidence.
99% confidence
Formula
=CONFIDENCE.NORM(0.01, 5, 50)
Returns 1.82 — a wider margin than 95% confidence because higher confidence requires a larger interval.
Common Errors
#NUM!
Alpha not between 0 and 1, standard deviation not positive, or sample size less than 1.
#VALUE!
Non-numeric arguments.
Tips
Same as CONFIDENCE
CONFIDENCE.NORM and CONFIDENCE are identical. The .NORM suffix clarifies it uses the normal distribution.
For small samples, use CONFIDENCE.T
When sample size is small (under 30) and the population std dev is estimated from the sample, CONFIDENCE.T using the t-distribution is more appropriate.
Required sample size
Rearrange the formula to find how many samples you need: n = (z * sigma / desired_margin)^2. For 95% confidence, z is approximately 1.96.
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