Apple Numbers STDEVA: Sample Standard Deviation with Text and Booleans
Calculate sample standard deviation in Numbers with explicit text and Boolean mappings, Apple’s temperature example and a transparent rescaling check.
Verification status: See the evidence and test scope described in this guide. How formula examples are checked
Quick answer: =STDEVA(A2:A7) returns approximately 2.6832815730 for the mixed entries below. It calculates sample standard deviation after mapping referenced text and FALSE to zero, TRUE to one, and ignoring genuinely empty cells.
Availability and version history
- Introduced: Original release and introduction date are not established by the checked references.
- Availability: Currently documented in Apple’s Functions Help 15.4 documentation edition; no final supported-version endpoint is established.
- Scope: Shared iWork reference. Function-specific historical availability across Mac, iPad, iPhone and iCloud is unverified.
- Checked: October 14, 2026, against the official STDEVA reference. Independent typed-data and mathematical checks; no native Numbers test.
Syntax, sample size and included types
=STDEVA(value, value…)
The first value or collection is required; further values or collections are optional. Apple specifies at least two values, including for the population functions. One range argument can contain multiple values. Do not confuse the number of arguments with the number of included observations.
Text is allowed in a referenced cell but is not allowed as a directly entered string argument. For example, put pending in a cell instead of constructing =STDEVA(4,6,"pending"). The latter is outside the documented argument contract; this guide does not assign it an unverified error token.
Apple’s Notes explicitly say included date/time or duration values produce an error. Keep those types out of these numeric examples, even though the argument description also mentions a general same-type rule.
Use STDEVA when the included values represent a sample of a larger population. STDEVPA uses the population definition. Deciding which observations belong in that population is separate from choosing a formula.
Build a visible numeric reconstruction
Create a Numbers table named MixedDispersion, with eight columns A:H and ten rows, including one header row. Use Input, Type, Mapped value and Squared deviation as A1:D1. Enter:
| Row | A: Input | B: Type | C: Mapped value |
|---|---|---|---|
| 2 | 4 | Number | 4 |
| 3 | 6 | Number | 6 |
| 4 | pending | Text | 0 |
| 5 | =TRUE() |
Boolean | 1 |
| 6 | =FALSE() |
Boolean | 0 |
| 7 | leave empty | Empty | leave empty |
Enter pending without quote characters. Leave A7 and C7 truly empty, rather than typing the instruction or a formula that returns empty text. The five numbers in C2:C6 are a manual reconstruction, not a claim that a pending measurement really equals zero.
In E2 enter =AVERAGEA(C2:C6) for 2.2. In D2 enter =(C2-$E$2)^2, and fill down through D6. The five squared deviations are 3.24, 14.44, 4.84, 1.44 and 4.84. Put =SUM(D2:D6) in E3 for 28.8.
Use G2:G6 for the results:
| Cell | Formula | Expected result |
|---|---|---|
| G2 | =STDEVA(A2:A7) |
2.6832815730 |
| G3 | =STDEVA(C2:C6) |
2.6832815730 |
| G4 | =SQRT(SUM(D2:D6)/4) |
2.6832815730 |
| G5 | =STDEV(A2:A3) |
1.4142135624 |
| G6 | =VARA(A2:A7) |
7.2 |
The reconstruction uses five included values, so its denominator is 4, or n−1. The square root puts the result back in the same units as the mapped numbers. G5 deliberately selects only the two original numbers, 4 and 6, to show a different data-selection policy. It does not silently treat all five mapped entries as measurements.
The printed decimals are rounded expected outputs. Keep unrounded formulas for later calculations; a display rounded to a few places should not become a replacement input.
Reproduce Apple’s missing-temperature example
Create SensorExample, with six columns and nine rows. In A1:C1 enter Day, High and Low. Use the following complete source data; enter the day labels as plain text and n/a as ordinary text without quotes, not a spreadsheet error value.
| Row | A: Day | B: High | C: Low |
|---|---|---|---|
| 2 | July 1 | 58 | 58 |
| 3 | July 2 | 84 | 61 |
| 4 | July 3 | 82 | 59 |
| 5 | July 4 | 78 | 55 |
| 6 | July 5 | n/a | n/a |
| 7 | July 6 | 81 | 57 |
| 8 | July 7 | 93 | 67 |
In E2 enter =STDEVA(B2:B8) for approximately 31.8067078879. In E3 enter =STDEV(B2:B8) for approximately 11.6218185611. These reproduce Apple’s stated comparison: the A-function includes the text marker as a seventh, zero-valued observation; the numeric-only calculation uses six recorded highs.
A missing temperature is not a measured 0 °F. The larger mixed-input result reflects that chosen conversion. Apple labels this an illustrative sample rather than a statistically justified sampling design. The arithmetic alone cannot establish representativeness.
Advanced: scale every mapped value consistently
Create RescaledSample, with five columns and eight rows. Enter 4,6,0,1,0 in A2:A6. In B2 enter =2*A2+10 and fill through B6, producing 18,22,10,12,10.
| Cell | Formula | Expected result |
|---|---|---|
| D2 | =STDEVA(A2:A6) |
2.6832815730 |
| D3 | =STDEVA(B2:B6) |
5.3665631460 |
| D4 | =D3/D2 |
2 |
| D5 | =VARA(B2:B6) |
28.8 |
| D6 | =AVERAGEA(B2:B6) |
14.4 |
Adding the same constant to every number leaves deviations from the mean unchanged. Multiplying every number by two doubles standard deviation and multiplies variance by four. Here the transformation is applied to the explicitly mapped numeric dataset, including its zeros and one.
Changing only the genuine measurements while leaving pending as text would be a different transformation: that text would still map to zero, rather than becoming ten. A conversion rule cannot decide whether the original type mixing was sensible.
Equal results do not certify clean data
Create ZeroControl, with three columns and six rows. Enter numeric 0 in A2:A3, text pending in A4 and =FALSE() in A5. Put =STDEVA(A2:A5) in C2 and =STDEV(A2:A3) in C3; both return 0. Text and a Boolean are present even though the dispersions match.
A difference between an A-function and a numeric-only calculation can reveal an inclusion-policy difference, as in the sensor example. Equality does not prove that every input is numeric, complete or valid. Inspect types and collection rules before interpreting the statistic.
Limits and related functions
Empty cells, text markers, numeric zeros and error cells are distinct inputs. The checked references do not establish every imported numeric-string representation, formula-produced empty string, native error label or numerical limit. Keep those cases separate from the explicitly typed examples above.
STDEV explains the corresponding numeric-only statistic. STDEVPA uses the other denominator choice. AVERAGEA explains the mapped mean, and SQRT connects variance to standard deviation.