NORM.S.INV in ONLYOFFICE: Standard Normal Quantiles and Critical Values

Create a sheet named PackingModel. These are assumed model parameters, not measurements or a fitted dataset. Put Parameter in A1 and Value in B1, then enter:

Keep this input block unchanged while adding formulas in D2 onward. Enter 0.95 as a number, or 95% with percentage formatting, rather than the number 95. The numeric value 0.95 and the percentage 95% represent the same probability; the plain number 95 does not.

A normal model is symmetric and continuous. It is an assumption about a distribution, not proof that actual packing data are normal, independent or stable. The parameters must be chosen or estimated separately. In particular, a standard deviation is not a standard error of a sample mean; substituting one for the other changes the modeled quantity.

The official reference takes one probability strictly between 0 and 1. It returns a dimensionless z-score for a distribution with mean 0 and standard deviation 1.

A probability of 0.975 is not the same as a central interval mass of 0.975. This function inverts a single left-tail CDF. To obtain symmetric bounds around zero, choose complementary lower and upper probabilities. The midpoint p=0.5 gives z=0; probabilities below 0.5 give negative values and those above 0.5 give positive values.

Keep PackingModel unchanged and use the following independent controls:

D7 converts a standard quantile into the original grams using mean+standard_dev×z. It is mathematically equivalent to =NORM.INV(0.95,B2,B3). D8 reverses the inverse through a CDF. Treat the result as an approximate numerical consistency check, not exact equality or proof that the distribution describes real observations.

Put 0.95 in F2 as the desired central model mass. Put =(1-F2)/2 in F3 for 0.025, and =1-F3 in F4 for 0.975. Then =NORM.S.INV(F3) and =NORM.S.INV(F4) produce approximately −1.95996398454005 and +1.95996398454005.

The corresponding fill limits are approximately 480.400360154599 and 519.599639845401 grams. Their total interval mass is 95% under the assumed model. Choosing p=0.95 directly gives the one-sided 95th percentile, whose symmetric pair encloses 90%, not 95%.

A practical check is symmetry: Φ⁻¹(1−p)=−Φ⁻¹(p). Another is monotonicity: a larger left-tail probability should correspond to a larger quantile. These identities can help detect swapped inputs or mistaken percentage scaling. They do not guarantee the accuracy of an extreme quantile or validate the statistical model.

If applying a z critical value to uncertainty in an estimated mean, the scale may be a standard error rather than the individual-observation standard deviation. That requires a separately justified inference procedure. This guide’s packing calculation uses the distribution of individual fills and makes no confidence-coverage claim for an estimated process mean.

The tagged source rejects probability≤0 or probability≥1 with #NUM!. Therefore =NORM.S.INV(0), =NORM.S.INV(1) and =NORM.S.INV(95) are source-predicted errors. Input 95% or 0.95 for a 95th-percentile request. Endpoint infinities are mathematical limits, not accepted return values for these arguments.

NORMSINV has the same one-argument role. The modern NORM.S.INV class directly inherits its calculation, including the strict domain check and gaussinv helper. This verified inheritance justifies one guide for the two names; it does not establish every deployment or imported-workbook behavior.

The code has an elementwise path for explicit arrays, but no array-entry or spill workflow was tested here. Conversion errors and invalid probabilities must be diagnosed before adding IFERROR. A fallback zero would look like a legitimate median z-score and could hide a serious invalid-input problem.

Probabilities stored extremely close to endpoints are sensitive to rounding and cancellation in earlier formulas. Keep the original numeric probability and confirm precision in the target build before relying on rare-event quantiles. These moderate examples are not an extreme-tail accuracy certification.

These notes were checked on October 18, 2026 against the linked official help and SDKJS tag v10.0.0.169, commit 3679e0375a758d7dc59d31337a45461be3525ed8. The original introduction date and final supported-version endpoint are not established. Current help is not a complete historical compatibility table; Desktop Editors, Docs and mobile releases need separate verification.

Neither the ONLYOFFICE application nor its calculation engine was executed for this guide. Numerical targets in this guide were independently calculated from normal-distribution mathematics; source observations come from reading the pinned implementation. Rounded targets are not promises of identical displayed digits. There are no new native screenshots or observed workbook results.

Examples use English names and comma separators. Adapt separators to the actual editor without changing argument order. Scalar examples do not establish dynamic-array spill, imported-workbook parity, every coercion rule or behavior in older releases. Retain unrounded probabilities for later calculations and format cells for display rather than replacing stored values with rounded text.

• Official NORM.S.INV help, checked October 18, 2026.
• Pinned calculation and name definitions.
• Pinned normal-distribution helpers.
• NORM.DIST.
• NORM.INV.
• NORM.S.DIST.
• NORMSDIST.