NORMINV Function (OpenOffice Calc)
Use OpenOffice NORMINV to turn a cumulative probability into a normal cutoff, with mean and SD inputs, tail selection and endpoint cautions.
Verification status: Official release source and independently calculated examples for NORMINV were reviewed. No new Apache OpenOffice Calc application execution is claimed for this guide; the stated version and example limits still apply. How formula examples are checked
Quick answer: =NORMINV(0.9;10;2) returns approximately 12.563103131089. It is the measurement cutoff below which 90% of the specified normal distribution lies, not a probability result.
Availability and version history
- Introduced: First release not established. This name is documented in the OpenOffice.org 3.3 function appendix; that establishes historical presence, not introduction.
- Supported in: Apache OpenOffice 4.1.16. No final supported release has been established.
- Scope: Desktop Calc. These examples use English function names, semicolon separators and decimal points.
- Checked: October 14, 2026. Official source/documentation and independent mathematical examples checked; Apache OpenOffice Calc was not run for this guide.
What NORMINV calculates
NORMINV reverses the cumulative form of NORMDIST. With probability p, mean μ and positive SD σ, the result is μ+σΦ⁻¹(p). It has the same units as μ and σ. A larger p produces a higher cutoff for fixed model parameters. Official reference.
An inverse CDF is different from a reciprocal. Dividing 1 by a normal probability does not find its percentile. Nor does NORMINV fit a distribution to data: the mean, SD and intended tail must already be specified.
Syntax and valid inputs
=NORMINV(Probability; Mean; StDev)
All three arguments are required. Use 0<Probability<1 and StDev>0 for a finite answer. A probability of 0.9 or a cell holding 90% means 90%; entering 90 means a value outside the probability domain. Mean may be negative, zero or positive.
The release evaluator treats probabilities outside [0,1] or a nonpositive SD as invalid arguments. The endpoint values 0 and 1 follow a separate no-value error path because the normal model has no finite cutoff there. This is source-based behavior; no native error display is asserted here.
Reproducible reference cases
Enter the formulas in separate cells. These independent mathematical values are rounded to twelve decimal places; retain unrounded stored results in subsequent calculations.
| Formula | Expected result, approximately |
|---|---|
=NORMINV(.5;10;2) |
10 |
=NORMINV(.9;10;2) |
12.563103131089 |
=NORMINV(.95;10;2) |
13.289707253903 |
=NORMINV(.975;10;2) |
13.91992796908 |
=NORMINV(.025;10;2) |
6.08007203092 |
=NORMINV(.75;10;2) |
11.348979500392 |
=NORMINV(.5;0;1) |
0 |
=NORMINV(.99;10;2) |
14.652695748082 |
Practical worksheet: find an upper cutoff
Put probability 0.9 in A2, mean 10 in B2 and SD 2 in C2. In D2 enter =NORMINV(A2;B2;C2). The expected cutoff is 12.563103131089. Format A2 as a percentage if useful, but format D2 as a measurement. Showing D2 as a percentage would obscure its meaning.
In E2 enter =NORMDIST(D2;B2;C2;1). It should return approximately 0.9. If it differs, check whether D2 was manually rounded, whether the same parameters were used, and whether NORMDIST’s last argument is cumulative rather than density mode.
Advanced example: a central 95% interval
A central 95% interval leaves 2.5% in each tail. For this model, use =NORMINV(0.025;10;2) and =NORMINV(0.975;10;2), giving approximately 6.080072030920 and 13.919927969080. Supplying 0.95 for the upper endpoint alone instead leaves 5% in the upper tail and does not define the same central interval.
Check the band with =NORMDIST(13.91992796908;10;2;1)-NORMDIST(6.08007203092;10;2;1), which is approximately 0.95. In an actual worksheet, reference the unrounded endpoint cells rather than these displayed decimal values.
This is an interval containing a specified portion of an assumed distribution. It is not automatically a confidence interval for an estimated population mean. The latter requires an appropriate inferential model, sample size and uncertainty treatment.
Errors, interpretation and precision
Keep the original probability cell. A value rounded to a displayed 100% might still be less than 1 internally, while replacing it with the literal number 1 changes it to an invalid finite-cutoff request. Quantiles become increasingly sensitive near the endpoints.
If the modeled variable cannot be negative, a normal model may still produce negative lower percentiles. The calculation is not necessarily wrong; the chosen distribution may be unsuitable. Review that assumption rather than clipping the result silently. Cross-check extreme probabilities and imported input types in the actual installed release when those cases matter.