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STDEV Function in Apple Numbers: Sample Standard Deviation

Statistical Intermediate Apple Numbers

Calculate sample standard deviation in Apple Numbers with exact syntax, sample data, independently checked results, and troubleshooting.

Verification status: Official documentation referenced; example outcomes independently reasoned or arithmetically checked where stated. Not executed in Apple Numbers. How formula examples are checked

Quick answer: =STDEV(B2:B9) estimates the sample standard deviation of eight measurements. With the precise data below its independently calculated expected result is 2.138089935299395, or 2.14 if the result cell shows two decimals. Standard deviation measures how far observations tend to lie from their mean; it does not give the average itself. [A1]

Exact Apple syntax and argument requirements

=STDEV(value, value…)
Apple argument Required? Official accepted inputs
value Yes A numeric, date/time or duration value, or a collection of those values. All included values must have the same value type. A total of at least two values is required.
value… No One or more further values or compatible collections/ranges.

The ellipsis is Apple’s notation for optional repeated values, not text you type into a formula. No other optional settings or defaults are documented. Apple defines STDEV as the square root of the sample variance and recommends its STDEVP function instead when the selected observations are the entire population. [A1]

A sample estimate uses denominator n − 1 when forming variance; population variance uses n. The difference matters especially for small samples. STDEV’s output has the same underlying measurement units as numeric input (unlike variance, which is in squared units). [A1; independent statistical calculation]

Platform, version, Numbers table references and regional syntax

This tutorial is based on Apple Formulas and Functions Help, documentation edition 15.4, with the function page accessed 2026-10-12. The edition label identifies the help material, not a proven minimum release of Numbers on every device. Apple provides separate release information for Mac; if you must support a particular historical app version, check that release’s availability before deploying this formula. [A1, R1]

All input examples use cells in one Numbers table, with ranges such as B2:B9. Numbers spreadsheets can contain multiple tables on a sheet; when sourcing another table, select its cells through the formula editor so Numbers constructs the table- or sheet-qualified reference. Do not paste Excel’s Sheet!A1 form and assume it means the same thing in Numbers. [R1, R2]

Locale: These formulas use English function names, . for decimal fractions and , between function arguments. If the system uses a comma decimal separator, Apple instructs using semicolons between arguments when formulas are typed manually. Colons identifying ranges, such as B2:B9, stay colons. Function-name capitalization is not significant. [R1]

Verification labels: Documentation-checked expected output means Apple published that exact example. Independently calculated expected output means the mathematical result was checked outside Numbers. Neither is an observation from running Apple Numbers; native Apple Numbers execution and screenshots were not available in this review.

Build a reproducible Numbers worksheet

  1. Add a table and name it MeasureLog. Make row 1 a header row with Trial in A1 and Measurement in B1. Ensure there are at least nine rows total.
  2. Enter the numbers exactly as shown; don’t append a unit like kg to numeric cells. Keep the result column outside B2:B9.
  3. Put the STDEV formula in D2. Format it to show extra decimal places for comparison.
Row A: Trial B: Measurement
2 1 2
3 2 4
4 3 4
5 4 4
6 5 5
7 6 5
8 7 7
9 8 9
=STDEV(B2:B9)

Independently calculated expected value: 2.138089935299395. The mean is 5; the sum of squared deviations from the mean is 32; there are eight observations. Thus sample variance = 32 / (8 − 1) = 4.571428571428571 and sample standard deviation is the square root: sqrt(32/7) ≈ 2.1380899353. The numbers were checked using an independent Python statistics calculation, not Apple Numbers. [A1, C1]

Check with a separate formula and a direct-argument example

Enter in an unused result cell Independently calculated expected result Reason
=VAR(B2:B9) 4.571428571428571 Related sample variance.
=STDEV(3,7) 2.8284271247461903 Two-point sample, mean 5, sum of squared deviations 8, n−1 = 1.
=STDEV(4,4,4) 0 No variation among three identical observations.
=STDEV(B2:B9,5) 2 Add one observation at the existing mean; sqrt(32/(9−1)) = 2.

For a locale with comma decimals, two numeric arguments can be entered as =STDEV(3;7). The single-range form is unaffected by the argument separator. These are expected outputs, not Numbers observations. [A1, R1, C1]

Advanced: compare sample and population spread

For the eight numbers in the worksheet, the population variance is 32 / 8 = 4, so the population standard deviation is 2. Apple’s distinct STDEVP function is intended for that interpretation. Keep the same range and compare =STDEV(B2:B9) with =STDEVP(B2:B9) only if your dataset truly corresponds to a sample versus the entire population. The question is about which observations exist, not which result looks more favourable. [A1, C1]

For a rolling quality log, maintain one numeric row per observation, and update the range deliberately as new rows are added. Avoid selecting the header row as a data value or accidentally including results back into the source range. If you compare two cohorts, the magnitude of STDEV alone doesn’t establish a performance improvement: different means, measurement units, and outliers also matter.

Independent cross-check against Apple’s published example

Apple publishes five scores in its first test column: 75, 100, 40, 80, 90. It reports =STDEV(A1:A5) as approximately 22.8035085019828. Its companion =VAR(A1:A5) is 520, and sqrt(520) matches the standard-deviation result. This is a documentation-checked example and independent arithmetic verification, not a runtime reproduction. [A1, A2]

Errors, limits and troubleshooting

  1. Only one eligible value: Apple requires at least two values. A one-item collection violates its documented input requirement; no specific on-screen error token is asserted here. [A1]
  2. Text or numbers stored as text: the expected results assume eight genuine numeric cells. Inspect imported values individually; this guide does not establish every coercion rule for text, blanks or Booleans. [A1]
  3. Mixed data types: Apple says STDEV accepts numbers, dates/times and durations, but all values supplied must use the same type. A mix of numeric counts and duration values does not satisfy that rule. [A1]
  4. Unexpectedly small spread: check whether you used STDEVP (population) rather than STDEV (sample). They divide the same squared-deviation total by different denominators. [A1]
  5. Wrong range: confirm B2:B9 contains exactly eight intended numeric observations, no summary rows or accidental outliers. Adding a result cell to the range can produce a circular reference.
  6. Display disagreement: spreadsheet cells may show rounded results. Increase displayed decimals before concluding the underlying numeric result is different.
  7. Migration: Excel’s modern sample-standard-deviation function is STDEV.S, whereas Apple calls its sample function STDEV. Their statistical purposes correspond; Excel’s text/coercion and error rules must be checked separately rather than assumed identical. [X1]

Source check date: 2026-10-12. Evidence classes: A1/A2 and R1/R2 are Apple documentation; C1 is independently calculated arithmetic; X1 is third-party-application official documentation. No native Numbers execution occurred.

Migration and validation limits

The formulas were not executed in a native Numbers app on Mac, iPad, iPhone or iCloud.com. Recheck display formatting, locale entry, data typing, empty/error cells, table references and older release support in the target installation. Test representative examples in the target app before relying on the worksheet. Do not use this tutorial as a substitute for reviewing the data collection method or domain-specific analysis assumptions.