Master the SKEW Function: Measuring Data Skewness in Excel
=SKEW(number1, [number2], ...)The SKEW function is a powerful statistical tool in Excel that measures the asymmetry of a probability distribution around its mean. Understanding skewness is crucial for data analysts, financial professionals, and researchers who need to assess whether their data distribution is symmetrical or tilted toward one side. Skewness values indicate the direction and degree of asymmetry: positive skewness means the tail extends to the right, negative skewness indicates a left-extending tail, and a value near zero suggests a symmetrical distribution. In business applications, skewness analysis helps identify unusual patterns in sales data, customer behavior, financial returns, and quality metrics. For instance, analyzing the skewness of product return rates or customer satisfaction scores can reveal whether most values cluster around the mean or if there are extreme outliers pulling the distribution in one direction. The SKEW function calculates the sample skewness using the third moment about the mean, making it an essential metric for exploratory data analysis, risk assessment, and hypothesis testing in advanced statistical work.
Syntax & Parameters
The SKEW function syntax is straightforward: =SKEW(number1, [number2], ...). The function requires at least one parameter but typically works with multiple data points for meaningful results. Number1 is the required first parameter, which can be a single cell, a range of cells (such as A1:A100), or an array of values. Number2 and subsequent optional parameters allow you to include additional individual numbers or ranges, enabling flexible data input methods. Key parameter details: Each number parameter can reference cells containing numerical values, ranges spanning multiple cells, or literal numbers entered directly into the formula. The function automatically ignores empty cells, logical values (TRUE/FALSE), and text entries within the specified ranges, focusing only on numerical data. However, if you include text or logical values as direct parameters, the formula returns a #VALUE! error. For accurate skewness calculations, ensure your dataset contains at least three data points, as skewness requires sufficient variation to be meaningful. The SKEW function calculates sample skewness using n-1 in the denominator, making it appropriate for sample data rather than entire populations (use SKEW.P for population skewness instead).
number1number2Practical Examples
Analyzing Sales Distribution Skewness
=SKEW(A2:A13)This formula calculates the skewness of the 12 sales values. A positive result indicates that higher-performing stores create a right-skewed distribution, suggesting most stores cluster around the lower-middle range with some high performers pulling the distribution rightward.
Evaluating Customer Response Time Distribution
=SKEW(B2:B16)This formula reveals how the single extreme outlier (12 minutes) affects the distribution shape. A positive skewness value indicates the outlier pulls the distribution rightward, showing that most responses are quick but occasional delays exist.
Assessing Test Score Distribution
=SKEW(C2:C21)This formula calculates skewness for the exam distribution. A negative value would indicate left skewness, meaning most students scored high with few low performers. This pattern is common in well-prepared classes or easier exams.
Key Takeaways
- SKEW measures distribution asymmetry, with positive values indicating right skewness, negative values indicating left skewness, and values near zero indicating symmetry.
- The function calculates sample skewness using n-1, making it appropriate for sample data; use SKEW.P for population-level analysis.
- Minimum three data points are required, but reliable skewness analysis typically requires 20+ observations to reduce sampling variability.
- SKEW automatically ignores empty cells and text in ranges but returns #VALUE! if text or logical values are passed as direct parameters.
- Combine SKEW with visualization tools and complementary functions like KURT for comprehensive distribution analysis and better decision-making.
Pro Tips
Use SKEW in combination with visualizations like histograms or box plots to validate skewness calculations visually, ensuring numerical results align with visual distribution patterns.
Impact : Combining numerical and visual analysis prevents misinterpretation of skewness values and helps communicate findings more effectively to stakeholders.
When analyzing business data with outliers, calculate skewness both with and without extreme values using separate formulas to understand how outliers influence distribution shape.
Impact : This approach reveals whether skewness is driven by genuine distribution characteristics or anomalous data points, informing data cleaning decisions.
For time-series data, calculate rolling skewness using a moving window (e.g., last 30 days) to track how distribution shape changes over time, revealing evolving patterns in your data.
Impact : Rolling skewness analysis identifies temporal trends in distribution characteristics, enabling early detection of operational changes or emerging issues.
Document skewness thresholds for your specific business context, as interpretation varies by industry; establish whether skewness >0.5 is concerning for your particular metrics.
Impact : Clear threshold documentation enables consistent interpretation across teams and supports automated alerting systems for anomalous distributions.
Useful Combinations
Comprehensive Distribution Analysis with Skewness and Kurtosis
=CONCATENATE("Skewness: ",ROUND(SKEW(A2:A50),3)," | Kurtosis: ",ROUND(KURT(A2:A50),3))This combination calculates both skewness and kurtosis in a single cell, providing complete distribution shape analysis. Skewness measures asymmetry while kurtosis measures tail heaviness, together revealing whether data follows a normal distribution or has extreme values.
Conditional Skewness Analysis with IF Statement
=IF(SKEW(A2:A50)>0.5,"Right-skewed distribution",IF(SKEW(A2:A50)<-0.5,"Left-skewed distribution","Approximately symmetric"))This formula calculates skewness and automatically interprets the result, returning descriptive text about the distribution shape. Useful for automated reporting and dashboards where you need interpretive labels rather than raw numbers.
Skewness with Statistical Significance Testing
=SKEW(A2:A50)/(SQRT(6/COUNT(A2:A50)))This combination calculates the skewness coefficient and divides by the standard error of skewness, providing a test statistic for evaluating whether skewness is statistically significant. Values beyond ±2 typically indicate significant skewness at the 0.05 level.
Common Errors
Cause: The formula includes text values, logical values (TRUE/FALSE), or non-numeric data as direct parameters, or fewer than three numerical values are provided in the range.
Solution: Verify all parameters contain only numerical data. Use ranges that exclude headers and text. Ensure at least three numeric values exist in your dataset. Use IFERROR to handle problematic data: =IFERROR(SKEW(A2:A10),"Check data")
Cause: The formula references a cell or range that has been deleted, moved, or doesn't exist in the current worksheet.
Solution: Check that all cell references are valid and the ranges haven't been deleted. Use the Name Manager to verify range names if using named ranges. Rebuild the formula with correct references.
Cause: This error rarely occurs with SKEW but can happen if the range contains only one or two unique values, preventing proper variance calculation.
Solution: Ensure your dataset has sufficient variation and at least three data points. Check for data entry errors or repeated values that might limit variation. Consider whether your dataset is appropriate for skewness analysis.
Troubleshooting Checklist
- 1.Verify all data in the range contains only numerical values; check for hidden text, spaces, or formatting issues that might prevent recognition as numbers.
- 2.Ensure the range includes at least three data points; SKEW cannot calculate meaningful results with fewer observations.
- 3.Confirm the referenced range hasn't been deleted or moved; use Ctrl+` to toggle formula view and verify references visually.
- 4.Check that you're using SKEW (not SKEW.P) if analyzing sample data, or SKEW.P if analyzing complete populations.
- 5.Test the formula on a small known dataset first to verify it returns expected results before applying to large datasets.
- 6.Use the Name Manager to verify named ranges are correctly defined if using named ranges instead of cell references.
Edge Cases
Dataset with all identical values (e.g., 5, 5, 5, 5, 5)
Behavior: SKEW returns #DIV/0! error because variance is zero and skewness cannot be calculated for data with no variation.
Solution: Check data for data entry errors or whether the metric actually varies in reality; if no variation exists, skewness analysis is not applicable.
This edge case indicates either measurement error or a metric that genuinely doesn't vary in your context.
Very large datasets (100,000+ rows) in memory-constrained environments
Behavior: SKEW calculates correctly but may slow worksheet recalculation or consume significant memory resources.
Solution: Consider using data sampling techniques or moving analysis to specialized statistical software if performance becomes problematic; alternatively, use SKEW.P for slightly faster calculation.
Modern Excel versions handle large datasets efficiently, but extremely large analyses may benefit from dedicated statistical tools.
Mixed positive and negative values with extreme outliers on both sides
Behavior: SKEW may return values near zero despite non-symmetrical distribution if outliers balance on both sides, potentially masking true distribution characteristics.
Solution: Visualize the distribution with histograms and calculate skewness on subsets to understand whether near-zero skewness reflects true symmetry or offsetting outliers.
Always combine numerical skewness with visual analysis to avoid misinterpretation of distribution shape.
Limitations
- •SKEW measures only asymmetry (third moment) and doesn't capture kurtosis (tail heaviness); use KURT function in combination for complete distribution analysis.
- •The function is sensitive to outliers; extreme values can dramatically shift skewness calculations, potentially misrepresenting the central distribution shape, requiring outlier analysis before interpretation.
- •SKEW requires at least three data points and produces unreliable results with small datasets (fewer than 20 observations); sample size significantly affects reliability and interpretation validity.
- •The function assumes numerical data and ignores text, logical values, and empty cells within ranges, but returns errors if these data types are passed as direct parameters, limiting flexibility with mixed-type data.
Alternatives
Compatibility
✓ Excel
Since 2007
=SKEW(number1, [number2], ...) - Fully supported in Excel 2007, 2010, 2013, 2016, 2019, and 365 with identical syntax and behavior.✓Google Sheets
=SKEW(value1, [value2], ...) - Google Sheets supports SKEW with identical functionality and syntax to Excel.Google Sheets implementation is fully compatible; results should match Excel calculations exactly for the same datasets.
✓LibreOffice
=SKEW(number1, [number2], ...) - LibreOffice Calc supports SKEW with the same syntax, though labeled as SKEW in function wizard.