NORMSDIST in ONLYOFFICE: Legacy Standard Normal Cumulative Probability

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 describes the standard normal CDF. Supply a dimensionless z-score. The distribution has mean 0 and standard deviation 1, so the value describes the share at or below that standardized position.

Do not add TRUE or FALSE to this signature. NORM.S.DIST is a different two-argument interface that supports both a CDF and a density. For ordinary scalar numeric inputs, NORMSDIST(z) and NORM.S.DIST(z,TRUE) target the same mathematical probability. NORM.S.DIST(z,FALSE) answers a different question.

In D2 enter =(B5-B2)/B3 for 1, and in D3 enter =(B4-B2)/B3 for −1. Keep the weights in grams separate from the unitless standardized values.

D6 is the modeled interval from 490 to 510 grams. D7 is the upper tail above 510, obtained by subtracting the left-tail result from 1. D8 gives half the distribution at the mean. Negative z values are ordinary valid inputs, not negative probabilities.

The CDF is increasing: moving the threshold to the right includes more mass. Its mathematical range is 0 to 1. That does not mean every observed dataset has this distribution, or that the calculated percentage is an empirical count of records in a sheet.

On a separate comparison area, place z values −2,−1,0,1,2 in F2:F6. In G2 enter =NORMSDIST(F2) and in H2 enter =NORM.S.DIST(F2,TRUE), then fill down. Their independent CDF targets are approximately 0.0227501319481792, 0.158655253931457, 0.5, 0.841344746068543 and 0.977249868051821.

The pinned source uses the same 0.5+gauss expression for these cumulative scalar branches, but different argument preparation and array paths. This supports the scalar mathematical comparison, not universal parser or array compatibility. Retain TRUE during a migration to the two-argument function; omitting the required mode or substituting FALSE changes the formula contract.

For a central interval of two standard deviations, =NORMSDIST(2)-NORMSDIST(-2) has target 0.954499736103642. The complementary two-sided tail is approximately 0.0455002638963584. This is a model area, not automatically a hypothesis-test p-value. A valid statistical test needs its own assumptions and statistic definition.

The source computes 0.5+gauss(z), with piecewise numerical approximations inside gauss. A nearby helper comment explicitly warns that this expression has cancellation errors for negative z below −4. The general-normal NORM.DIST function instead uses an erfc-based CDF helper. Do not advertise the two forms as bit-for-bit equivalent in rare tails merely because they represent the same mathematical function.

Likewise, subtracting a CDF close to 1 can lose information about a very small upper tail. Moderate examples here do not establish rare-event accuracy. A displayed zero may be rounding or numerical loss, not mathematical impossibility. Confirm the exact target implementation before using extreme tails for consequential decisions.

Nonnumeric conversion errors propagate in the inspected source, and a computed NaN is mapped to a numeric error. No second mode argument is accepted by the declared signature. If standardization fails because the assumed standard deviation is zero, repair the input model rather than applying an error mask to NORMSDIST.

The code contains explicit array handling, but these examples are scalar and no application array workflow was tested. Legacy naming alone does not establish introduction or removal dates. Keep the source model, formula syntax and runtime observations clearly separate when documenting a migration.

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