What is the difference between SKEW and SKEW.P functions?

SKEW calculates sample skewness, applying Bessel's correction suitable for analyzing a subset of data. SKEW.P calculates population skewness, treating your dataset as the complete population without correction. Use SKEW.P when analyzing entire populations or complete datasets, and use SKEW when working with samples from larger populations.

How do I interpret skewness values returned by SKEW.P?

Skewness values indicate distribution asymmetry: values near 0 suggest symmetric distribution, positive values (>0.5) indicate right skewness with tail extending right, negative values ( 2 or <-2) indicate highly skewed distributions.

Can SKEW.P handle negative numbers and zeros in my dataset?

Yes, SKEW.P processes negative numbers and zeros normally as valid numeric values. The function calculates skewness based on the distribution pattern regardless of whether values are positive, negative, or include zeros. Ensure all values are properly formatted as numbers for accurate calculation.

What should I do if SKEW.P returns an error in my spreadsheet?

First, verify your data range contains at least three numeric values. Check for text entries, empty cells, or error values using Find & Replace. Ensure cell formatting is set to 'Number' rather than 'Text'. Confirm you're using Excel 2013 or later. Try removing outliers temporarily to test data integrity. Use IFERROR function to handle errors gracefully in reports.

Can I use SKEW.P with filtered or sorted data?

Yes, SKEW.P works with filtered data and calculates based on visible cells only when using AutoFilter. However, if you sort data, SKEW.P recalculates based on the new row positions, so ensure your range references remain correct after sorting. For complex filtering scenarios, consider using helper columns or creating separate analysis ranges.

Master SKEW.P: The Complete Guide to Population Skewness Analysis in Excel

Advanced

=SKEW.P(number1, [number2], ...)

The SKEW.P function is a powerful statistical tool in Excel that calculates the skewness of a population distribution. Skewness measures the asymmetry of a probability distribution around its mean, indicating whether data is concentrated on one side. Unlike SKEW (which calculates sample skewness), SKEW.P specifically computes population skewness, making it essential for analyzing complete datasets rather than samples. This function is particularly valuable in business analytics, financial analysis, and quality control applications where understanding data distribution characteristics is critical for decision-making. Skewness values range from negative to positive, with negative values indicating left-skewed distributions and positive values indicating right-skewed distributions. A skewness value near zero suggests a relatively symmetric distribution. By using SKEW.P, analysts can quickly identify distributional anomalies that might affect statistical conclusions and business strategies. This function integrates seamlessly with other Excel statistical functions, enabling comprehensive data analysis workflows that drive informed business insights.

Syntax & Parameters

The SKEW.P function follows the syntax: =SKEW.P(number1, [number2], ...). The 'number1' parameter is required and represents the first value or range containing numeric data for which you want to calculate population skewness. This can be a single cell reference, a range of cells, or an array of values. The 'number2' and subsequent optional parameters allow you to include additional numbers or ranges, enabling flexibility in data selection. You can include up to 254 arguments total in Excel. When using ranges, SKEW.P automatically ignores empty cells, logical values, and text entries, processing only numeric values. If your range contains mixed data types, ensure numeric values are properly formatted. You can also use multiple non-contiguous ranges by separating them with commas or semicolons (depending on your regional settings). For optimal results, ensure your dataset contains at least three values, as skewness calculations require sufficient data points for meaningful statistical interpretation. The function returns a decimal number representing the population skewness coefficient, which can be positive, negative, or zero.

number1

First number or range

number2

Additional numbers

Optional

Practical Examples

Sales Performance Distribution Analysis

=SKEW.P(B2:B13)

This formula calculates the population skewness of sales data from cells B2 through B13, representing 12 stores' monthly sales figures. The result indicates whether sales are concentrated at the lower end (negative skewness) or higher end (positive skewness) of the distribution.

Product Quality Control Measurements

=SKEW.P(C2:C501)

This formula evaluates the skewness of 500 product measurements stored in cells C2 through C501. A skewness value close to zero indicates the manufacturing process produces consistent, normally distributed dimensions.

Employee Salary Distribution Assessment

=SKEW.P(D2:D251)

This formula calculates population skewness for the entire employee salary dataset. A positive skewness would suggest higher salaries are outliers, while negative skewness would indicate lower salaries are outliers.

Key Takeaways

SKEW.P calculates population skewness, measuring distribution asymmetry for complete datasets without sample correction
Skewness values indicate distribution shape: near 0 for symmetric, positive for right-skewed, negative for left-skewed distributions
Minimum three numeric values required; function ignores empty cells, text, and logical values automatically
Available in Excel 2013 and later versions; use SKEW function as alternative for sample skewness calculations
Combine SKEW.P with other statistical functions (KURT, AVERAGE, STDEV.P) for comprehensive distribution analysis

Pro Tips

Use SKEW.P in data validation rules to flag datasets with unexpected skewness patterns, creating automated quality checks that alert analysts when distribution characteristics deviate from normal parameters.

Impact : Enables proactive data quality monitoring and early detection of data collection errors or unusual business patterns

Combine SKEW.P with conditional formatting to create visual heatmaps showing skewness severity across multiple datasets, making distribution patterns immediately visible in spreadsheets.

Impact : Dramatically improves data interpretation speed and makes statistical insights accessible to non-technical stakeholders

Create a sensitivity analysis by recalculating SKEW.P while temporarily excluding potential outliers using IF statements, helping distinguish between genuine skewness and outlier-driven asymmetry.

Impact : Provides deeper understanding of whether skewness is inherent to the process or caused by anomalous data points requiring investigation

Document your SKEW.P calculations with cell comments explaining the business context and interpretation thresholds, enabling team members to understand statistical significance beyond the numeric result.

Impact : Improves collaboration and ensures consistent interpretation of statistical findings across your organization

Useful Combinations

Distribution Analysis Dashboard

=CONCATENATE("Skewness: ",ROUND(SKEW.P(A2:A101),3)," | Kurtosis: ",ROUND(KURT(A2:A101),3)," | Mean: ",ROUND(AVERAGE(A2:A101),2))

This combination creates a comprehensive distribution summary by merging SKEW.P with KURT and AVERAGE functions, providing skewness, kurtosis, and mean in a single readable output for executive dashboards or reports.

Conditional Skewness Analysis

=IF(ABS(SKEW.P(B2:B51))>1,"Highly Skewed",IF(ABS(SKEW.P(B2:B51))>0.5,"Moderately Skewed","Approximately Symmetric"))

This formula uses nested IF statements with SKEW.P to automatically classify distribution characteristics, enabling automated reporting that categorizes data symmetry levels for quick interpretation.

Multi-Group Skewness Comparison

=SKEW.P(IF(C2:C101="Group A",D2:D101)) combined with similar formulas for Group B and C

This array formula approach calculates skewness separately for filtered groups within a larger dataset, enabling comparative analysis across different categories or segments in a single dataset.

Common Errors

#DIV/0!

Cause: The function receives fewer than three numeric values in the dataset, as skewness calculation requires minimum three data points for mathematical validity.

Solution: Verify your data range contains at least three numeric values. Check for empty cells, text entries, or logical values that might be excluded. Expand your dataset or use SKEW function documentation to confirm minimum data requirements.

#VALUE!

Cause: One or more arguments contain non-numeric data types such as text strings, dates formatted as text, or error values from other formulas.

Solution: Convert all text-formatted numbers to actual numeric format using VALUE function. Remove or filter out text entries. Ensure referenced cells contain only numeric data. Check for #N/A or #REF! errors in source data.

#NAME?

Cause: The function name is misspelled as 'SKEWP', 'SKEW_P', or other variations, or the formula is entered in an Excel version prior to 2013 that doesn't support SKEW.P.

Solution: Verify correct spelling: SKEW.P with a period separator. Check your Excel version (requires 2013 or later). Update Excel if necessary. Use SKEW function as alternative in older versions.

Troubleshooting Checklist

1.Verify your data range contains at least 3 numeric values; SKEW.P cannot calculate with fewer data points
2.Check that all cells in your range are formatted as 'Number' or 'Currency', not 'Text', using Format Cells dialog
3.Confirm Excel version is 2013 or later; SKEW.P is not available in Excel 2010 or earlier versions
4.Remove or convert any text-formatted numbers in your range using VALUE function or Find & Replace with regular expressions
5.Verify the formula syntax exactly: =SKEW.P(range) with period separator, not underscore or other characters
6.Check for hidden rows or filtered data that might affect your intended analysis range; use Unhide All to verify

Edge Cases

Dataset contains only identical values (e.g., all cells equal 10)

Behavior: SKEW.P returns 0, as there is no asymmetry in a perfectly uniform distribution

This is mathematically correct; zero skewness for uniform data is expected behavior

Range includes cells with scientific notation or very large numbers exceeding typical ranges

Behavior: SKEW.P processes these normally as long as they're formatted as numbers; results remain mathematically valid

Solution: Verify number formatting and consider data scaling if numbers differ drastically in magnitude

Excel handles scientific notation automatically; no special treatment required

Formula references a range that includes error values from other formulas (#N/A, #DIV/0!)

Behavior: SKEW.P returns #VALUE! error, propagating the error from source formulas

Solution: Use IFERROR to wrap source formulas or use AGGREGATE function to ignore errors in calculations

Always validate source data before using in statistical calculations

Limitations

•SKEW.P requires minimum three numeric values; cannot calculate with smaller datasets, limiting applicability to very small sample analyses
•Function treats all data points as population, not sample; users must manually choose between SKEW and SKEW.P based on data context, which can lead to incorrect statistical conclusions if misapplied
•SKEW.P cannot directly handle text-formatted numbers or dates; requires preprocessing to convert data types, adding workflow complexity in real-world scenarios
•Function provides only skewness coefficient without confidence intervals or significance testing; additional statistical analysis required for rigorous hypothesis testing or inferential statistics

Alternatives

SKEW Function

Calculates sample skewness with Bessel's correction, more appropriate for analyzing sample data from larger populations

When: When working with survey samples, experimental data, or any dataset representing a subset of a larger population

KURT Function

Measures kurtosis (distribution peakedness) rather than skewness, providing complementary distribution insights

When: When analyzing tail behavior and distribution concentration, often used alongside skewness for comprehensive distribution analysis

Combination of AVERAGE and STDEV.P

Provides mean and standard deviation for manual skewness calculation or creating custom distribution metrics

When: When building custom statistical dashboards or when skewness alone is insufficient for your analytical needs

Compatibility

✓ Excel

Since Excel 2013

=SKEW.P(number1, [number2], ...)

✓Google Sheets

=SKEW(range, TRUE) or use SKEW with population parameter

Google Sheets uses SKEW function with optional population parameter; SKEW.P syntax differs slightly but provides equivalent functionality

✓LibreOffice

=SKEW(range) with population mode or equivalent function

Frequently Asked Questions

Ready to master advanced statistical analysis in Excel? Explore ElyxAI's comprehensive Excel formula guides and unlock powerful data insights with step-by-step tutorials and real-world examples.

SKEW

KURT