VARP in Zoho Sheet: Population Variance and Squared Units

Use VARP in Zoho Sheet to calculate the variance of a complete population, dividing the sum of squared deviations by n. The working example below describes ticket handling times; its result is measured in squared minutes. The official reference supports the function’s identity and purpose.

This guide separates documentation review from application testing. The expected numbers are independently calculated, not screenshots or results captured from Zoho Sheet.

VARP(number; [number1]; ...)

Numeric inputs: individual numbers or cell ranges holding the observations. One range can contain many observations; a header is not a measurement.

Additional inputs: optional extra numeric values or ranges. Omit the square brackets shown in the syntax notation when entering a formula. The numeric-only examples avoid relying on text or logical-value conversion.

Use population variance to describe the spread of every observation in a specifically defined population. It gives equal weight to each included numeric observation. A row containing a subtotal is not interchangeable with a row containing an individual observation; mixing those two levels changes the question.

Variance is in squared units. It becomes one hundred times larger when every observation is multiplied by ten, even though the underlying pattern is unchanged. This makes it useful in calculations but easy to mislabel in a dashboard. Use a matching standard deviation measure when readers need a spread expressed in the original units.

A browser-based service register with one row per handled ticket provides the scenario. Enter the following numeric observations into A2:A10, one per row, with a header in A1. Use a spare result cell outside the input range.

Download the Zoho Sheet VARP practice data (CSV). Import it into a new, blank sheet starting at A1, with a comma as the file delimiter and the header row retained. The CSV contains input values only, not formulas or native test results. Blank spacer columns place the requested position in D2 and any additional test data in column F. Keep observations numeric rather than importing them as text.

Example 1: Measure spread in the working register

=VARP(A2:A10)

Expected result: 32.

The numeric count is 9, the mean is 12, and the sum of squared deviations is 288. Divide by 9 (n); the result is a variance in squared measurement units.

Example 2: Check the denominator with three known numbers

=VARP(2;4;6)

Expected result: 2.666667 (displayed to six decimal places).

The mean is 4 and the squared deviations are 4, 0 and 4, for a total of 8. The population denominator is 3. Keep the fractional result during later calculations rather than rounding the input values.

Example 3: Check squared units when values are rescaled

For this check, use F2:F5 containing 0, 0, 80, 80, in that order. These values are included in the practice file.

=VARP(F2:F5)

Expected result: 1600.

These four observations have mean 40 and squared deviations totalling 6400. They are ten times the numbers 0, 0, 8, 8, so their variance is one hundred times as large. Both zeros still count as observations.

Subtract the mean from each observation and square each difference. Add the squared differences and divide by n. Do not take a square root when reporting variance; its units remain squared. This mathematical definition is used to check the numbers in the examples, independently of the spreadsheet software. Keep stored results at full available precision and round only the final presentation when a report requires it. The displayed expectations above are rounded where necessary; a different number of displayed decimal places is not by itself a calculation error.

The output is labelled with ordinary units. A variance of 16 from data in minutes is 16 minutes squared. Its square root, 4, is the population standard deviation in minutes.

The result changes after rescaling the data. That is expected: multiplying each input by a factor multiplies variance by the square of that factor. Merely changing display formatting, without changing stored values, is a different operation.

The range includes summary rows or missing-value replacements. A subtotal introduces an extra observation, and replacing an unknown value with zero invents a measurement. Define the inclusion rule and inspect the data before relying on the resulting variance.

The documented Zoho name here is VARP. Its statistical role corresponds to VAR.P in the newer Excel naming scheme, but this guide does not assert that the dotted name is a supported Zoho alias.

The worked inputs contain only explicit numeric observations. A generic error-description block in the help is not treated as proof that every listed error applies to this particular formula. See the function-specific Zoho help.

The formulas follow Zoho’s documented semicolon-separated form. Do not replace the colon inside a range such as A2:A10: it still means all the cells between those endpoints. If your configured editor expects a different argument separator, use the one it displays.

In the service-register example, keep ticket handling time separate from time waiting for a customer. Record one intended observation per row before summarising the list. The function cannot tell which part of a ticket lifecycle a number represents. Native Zoho runs, mixed-type coercion tests and desktop/mobile comparisons are outside this guide’s verification scope.

For the same statistical question in other programs, see VAR.P in Microsoft Excel, VAR.P in Google Sheets, VAR.P in ONLYOFFICE. These links identify corresponding measures, not a guarantee that names, mixed-data rules, error codes or file conversions are identical.

MEDIAN, LARGE, SMALL, RANK.EQ, STDEV, STDEVP, VAR. Browse the Zoho Sheet function library for the other published guides.

Editorial verification: The vendor documentation was reviewed on 2026-10-12. Scope: Zoho Sheet public web help and its documented English formula names. Desktop/mobile parity and first-release dates have not been established here. Three numeric examples were checked with an independent arithmetic model. No native application run was performed. Single-value population edge cases, mixed-data coercion, exact error-code parity and round-trip workbook compatibility remain untested; they are not labelled application-verified.