CONFIDENCE Function (Calligra Sheets)

• Introduced: First release not established; the name is present in Calligra 2.4.0 source.
• Supported in: Calligra Sheets 26.08.2, with no retirement established.
• Scope: Desktop Sheets with the Statistical module. Examples use English semicolon-separated formulas.
• Checked: October 14, 2026. Selected formulas ran through the official 25.04.2 engine library. Source and independent mathematics were checked. This is engine evidence, with no GUI-cell or import verification; 26.08.2 was source-checked only.

CONFIDENCE returns the half-width of a two-sided normal-model confidence interval for a mean: z_(1−alpha/2)×sigma/√n. It does not return both endpoints or the sample mean. The interval is the independently supplied sample mean plus or minus this margin. Function help.

=CONFIDENCE(alpha;standardDeviation;sampleSize)

Exactly three arguments are required. Use 0<alpha<1, a positive known population standard deviation and a positive integer sample size. The implementation checks size≥1 but does not enforce integrality; the diagnostic reference at 100.9 shows that its square root uses the fractional value. A real sample still contains an integer number of observations. Registration and implementation.

The engine and mathematical columns are separate. Results are rounded to twelve decimal places, or thirteen significant digits in scientific notation for very small or large values. This is a display choice, not an accuracy guarantee. NaN and infinity are nonfinite outputs; an engine error flag is checked separately from those values.

With alpha=0.05, known sigma=10 and n=100, the margin is approximately 1.959963984540. If the observed mean is 50, the resulting interval is approximately [48.040036015460,51.959963984540]. This interpretation assumes independent observations and a normal population model, or a justified normal approximation.

When sigma is estimated from a small normal sample, a Student t construction accounts for that estimation differently. Choosing this function solely because a sample is called “large” or “small” is not a substitute for checking the model assumptions.

Holding alpha and sigma fixed, multiplying n by four halves the margin. The displayed native ratio compares n=100 with n=25 and returns 0.5. Reducing alpha from 0.05 to 0.01 increases the required quantile and widens the interval; it does not make the observed data less variable.

The confidence level describes the long-run coverage of the interval procedure under its assumptions. It is not the probability that this fixed population mean lies in one already computed interval, nor a prediction interval for a future individual observation.

The executed advanced check was:

=CONFIDENCE(0.05;10;100)/CONFIDENCE(0.05;10;25)

Its engine result was 0.5.

The wrapper rejects alpha endpoints, nonpositive sigma and size below 1. It accepts n=1 as an arithmetic case, which does not by itself make a normal approximation justified. A fractional sample size also passes, so validate actual counts before calculating. No automatic outlier removal, missing-data policy or population-model validation is performed by this formula.

• Function-specific implementation
• Versioned function help
• Normal Inverse helper

The statistical module and value-calculation helpers are byte-identical between the inspected 25.04.2 and 26.08.2 sources. That comparison supports the source discussion but does not establish later-version native output, every locale or arbitrary extreme-parameter accuracy.

NORMINV, SQRT.