MODE.SNGL in Excel: Most Frequent Numbers, Real Zeros and No-Repeat Errors

• Introduced: the Microsoft function catalogue marks MODE.SNGL as an Excel 2010 addition; an exact first build or preview date is not established here.
• Availability: official help lists Excel for Microsoft 365, Excel for Microsoft 365 for Mac, Excel 2024, Excel 2024 for Mac, Excel 2021, Excel 2021 for Mac, Excel 2019, Excel 2016.
• Checked: October 14, 2026; official MODE.SNGL reference. Documentation and independently modeled examples; no native Excel execution or broader platform certification.

=MODE.SNGL(number1, [number2], ...)

Use numeric values or ranges. Microsoft says text, logical values and empty cells within a reference are ignored, but numeric zeros count. Direct errors and untranslatable text are documented to cause errors; this guide does not extend that sentence into a complete error-containing-range matrix.

The primary examples have one unique most-frequent value. The dedicated MODE.SNGL page does not state a universal tie-break rule for several equally frequent modes, so that selection remains unverified here.

Enter 2, 2, 2, 5, 5 and 9 in A2:A7:

=MODE.SNGL(A2:A7)

Expected result: 2. The number 2 occurs three times, 5 occurs twice and 9 once. The mode is the repeated value itself, not the count 3 or the row containing the first 2.

The expected mean is 25/6, approximately 4.166667, and the median is 3.5. Different centers can all be mathematically correct for the same dataset while summarizing different aspects of it.

=MODE.SNGL(0,0,3,5)

Expected result: 0. The two zeros are actual observations and are the only repeated value.

Deleting them would leave 3 and 5, which have no repeated value and produce the documented no-mode error. A zero result therefore must not automatically be interpreted as missing data or suppressed as a blank.

Enter this independent fixture in D2:D8:

=MODE.SNGL(D2:D8) has expected result 4. The documented numeric reference values are 4,4,7,0; text 7 and the Boolean do not add observations to that numeric frequency table.

If the text 7 is deliberately converted into numeric 7, the comparison group would instead have two equally frequent modes, 4 and 7. That is a change in the data, not a formatting-only edit. This guide does not certify which tied value MODE.SNGL returns in every case.

=MODE.SNGL(1,2,3,4)

Documented expected error: #N/A. All four numeric values occur once. Their average is 2.5, but that does not create a repeated value for MODE.SNGL.

An explicit display policy can label the no-result case through IFNA, while preserving the original calculation elsewhere. A fallback label should not claim a particular cause unless the input range has also been checked for source errors.

Start with 2,2,2,5,5,9, whose unique mode is 2. Add two more observed values of 5. The expanded frequency table has 2 occurring three times and 5 occurring four times, so the unique mode becomes 5.

The mean changes from 25/6 to 35/8 = 4.375, but the mode jumps from 2 to 5. A discontinuous mode is expected when frequency leadership changes; it is not evidence of a rounding malfunction.

Keep the included period and records fixed when comparing modal values between reports. The function cannot decide whether new rows belong in the same population.

For 1.01, 1.04, 1.09 and 2, no exact numeric value repeats, so the raw mode has the documented #N/A expectation.

If a stated reporting policy groups measurements to one decimal place, a separate ROUND helper produces 1, 1, 1.1 and 2. That transformed dataset has unique mode 1.

A one-decimal number format does not perform this transformation. Use a visible numeric helper when rounding is part of the analysis, and retain the originals.

MODE.MULT is documented to return multiple modes as an array. Use it when several equally frequent values are substantively important, rather than assuming one output represents the full distribution.

Symmetry alone does not guarantee that mean, median and a single mode are identical; a symmetric dataset can have two modes. The dedicated page’s broad central-tendency commentary should not replace inspection of the actual frequency distribution. These examples certify arithmetic models and documented input rules, not native tie selection, exact error parity or every imported type.

• Microsoft MODE.SNGL reference.
• Editorial testing standards.

Expected results are independent statistical/arithmetic models or explicitly documented rules, not captured native output. Examples use English function names and comma separators. Datasets and reporting policies are fictional.