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NORM.S.DIST

Supported, behaves as documented

Category: Statistical · Last tested 2026-09-01

Real compatibility results for the NORM.S.DIST function: executed in Excel for the web, Google Sheets and LibreOffice Calc, with desktop Excel behavior from Microsoft’s official documentation (we do not run desktop Excel — Excel for the web is a different application and is executed separately). Syntax and links to that documentation are below.

Support matrix

EngineDocumentedLive-testedVerdict
Excel (desktop)Yes No — documented only n/a
Excel for the web— Yes (recalc, 2026-09-01) Supported, behaves as documented
Google SheetsYes Yes (Drive import, 2026-08-31) Supported, behaves as documented
LibreOffice CalcYes Yes (25.8.7.3, 2026-08-31) Supported, behaves as documented

LibreOffice version history

We executed the same test cases under each LibreOffice release to show exactly when NORM.S.DIST’s support changed — not documentation claims, real results.

LibreOffice versionVerdictTested
24.2.0.3 Supported, behaves as documented 2026-08-31
24.8.7.2 Supported, behaves as documented 2026-08-31
25.2.0.3 Supported, behaves as documented 2026-08-31
25.8.7.3 Supported, behaves as documented 2026-08-31

Executed test cases

Excel for the web (executed 2026-09-01 via OneDrive recalculation)

These values come from Excel for the web, not from desktop Excel. They are two different implementations of the calculation engine, and this run measured only the web one: the corpus was uploaded to OneDrive as .xlsx, recalculated by Excel for the web on open, and downloaded again for readback. Excel for the web is a rolling service with no pinnable version, so the run is identified by its date. Where a value here disagrees with the Expected column — which is Microsoft’s documentation of the desktop product — we cannot tell you whether the web engine diverges from the desktop one or the documentation is wrong about both, because we do not run desktop Excel.

FormulaDescriptionResultExpectedVerdict
=ROUND(NORM.S.DIST(1.333333,TRUE),6) Microsoft's documented example: standard normal cumulative distribution at z = 1.333333 0.908789 0.908789
Provenance

Microsoft publishes this example's result as 0.908788726. Independently verified against the standard normal CDF: Phi(1.333333) = 0.908788726 -> 0.908789 at 6 dp

Matched
=ROUND(NORM.S.DIST(1.333333,FALSE),6) Same z with cumulative FALSE returns the standard normal density 0.16401 0.16401
Provenance

Microsoft publishes this example's result as 0.164010148. Independently verified from the standard normal density exp(-z^2/2)/sqrt(2*pi) = 0.164010148 -> 0.164010 at 6 dp

Matched
=ROUND(NORM.S.DIST(0,TRUE),7) The standard normal CDF at zero is one half 0.5 0.5
Provenance

The standard normal is symmetric about 0, so Phi(0) = 0.5 exactly

Matched
=ROUND(NORM.S.DIST(-1.333333,TRUE)+NORM.S.DIST(1.333333,TRUE),7) Symmetry check: Phi(-z) + Phi(z) must be exactly 1 1 1
Provenance

Phi(-z) = 1 - Phi(z) for the standard normal, so the two cumulative values sum to 1 for any z

Matched
=NORM.S.DIST("x",TRUE) A non-numeric z is a value error #VALUE! #VALUE!
Provenance

'If z is nonnumeric, NORM.S.DIST returns the #VALUE! error' -- the only error condition documented for this function

Matched

Google Sheets (executed 2026-08-31 via Drive import)

Google Sheets is a rolling service with no pinnable version, so this run is identified by its date. The corpus was imported to Drive as .xlsx, recalculated by Sheets, and exported back for readback.

FormulaDescriptionResultExpectedVerdict
=ROUND(NORM.S.DIST(1.333333,TRUE),6) Microsoft's documented example: standard normal cumulative distribution at z = 1.333333 0.908789 0.908789
Provenance

Microsoft publishes this example's result as 0.908788726. Independently verified against the standard normal CDF: Phi(1.333333) = 0.908788726 -> 0.908789 at 6 dp

Matched
=ROUND(NORM.S.DIST(1.333333,FALSE),6) Same z with cumulative FALSE returns the standard normal density 0.16401 0.16401
Provenance

Microsoft publishes this example's result as 0.164010148. Independently verified from the standard normal density exp(-z^2/2)/sqrt(2*pi) = 0.164010148 -> 0.164010 at 6 dp

Matched
=ROUND(NORM.S.DIST(0,TRUE),7) The standard normal CDF at zero is one half 0.5 0.5
Provenance

The standard normal is symmetric about 0, so Phi(0) = 0.5 exactly

Matched
=ROUND(NORM.S.DIST(-1.333333,TRUE)+NORM.S.DIST(1.333333,TRUE),7) Symmetry check: Phi(-z) + Phi(z) must be exactly 1 1 1
Provenance

Phi(-z) = 1 - Phi(z) for the standard normal, so the two cumulative values sum to 1 for any z

Matched
=NORM.S.DIST("x",TRUE) A non-numeric z is a value error #VALUE! #VALUE!
Provenance

'If z is nonnumeric, NORM.S.DIST returns the #VALUE! error' -- the only error condition documented for this function

Matched

LibreOffice Calc 25.8.7.3 (tested 2026-08-31)

FormulaDescriptionResultExpectedVerdict
=ROUND(NORM.S.DIST(1.333333,TRUE),6) Microsoft's documented example: standard normal cumulative distribution at z = 1.333333 0.908789 0.908789
Provenance

Microsoft publishes this example's result as 0.908788726. Independently verified against the standard normal CDF: Phi(1.333333) = 0.908788726 -> 0.908789 at 6 dp

Matched
=ROUND(NORM.S.DIST(1.333333,FALSE),6) Same z with cumulative FALSE returns the standard normal density 0.16401 0.16401
Provenance

Microsoft publishes this example's result as 0.164010148. Independently verified from the standard normal density exp(-z^2/2)/sqrt(2*pi) = 0.164010148 -> 0.164010 at 6 dp

Matched
=ROUND(NORM.S.DIST(0,TRUE),7) The standard normal CDF at zero is one half 0.5 0.5
Provenance

The standard normal is symmetric about 0, so Phi(0) = 0.5 exactly

Matched
=ROUND(NORM.S.DIST(-1.333333,TRUE)+NORM.S.DIST(1.333333,TRUE),7) Symmetry check: Phi(-z) + Phi(z) must be exactly 1 1 1
Provenance

Phi(-z) = 1 - Phi(z) for the standard normal, so the two cumulative values sum to 1 for any z

Matched
=NORM.S.DIST("x",TRUE) A non-numeric z is a value error #VALUE! #VALUE!
Provenance

'If z is nonnumeric, NORM.S.DIST returns the #VALUE! error' -- the only error condition documented for this function

Matched

Docs & syntax