The SKEW function in Google Sheets is a powerful statistical tool that quantifies the asymmetry of a dataset’s distribution in relation to its mean. By calculating the skewness, users can gain insights into how data is spread and identify potential biases in the dataset, making it essential for data analysis and interpretation.

Syntax

SKEW(value1, [value2, ...])

• value1: The first value or range of values for which the skewness will be calculated.
• value2: (Optional) Additional values or ranges to include in the skewness calculation.

Example #1

=SKEW(A1:A10)

This function call calculates the skewness of the values within the range A1 to A10. For instance, if the dataset contains values such as 2, 3, 4, 5, 5, 6, 7, 8, 9, and 10, the function might return a skewness of 0.15, indicating a slight rightward skew in the distribution.

Example #2

=SKEW(A1:A10, B1:B10)

In this example, the function computes the skewness for the combined values from ranges A1:A10 and B1:B10. If A1:A10 holds values from 1 to 10 and B1:B10 holds values from 5 to 15, the output could be 0.45, suggesting a moderate right skew due to the additional higher values.

Example #3

=SKEW(15, 20, 25)

This call calculates the skewness for a specific set of three individual values: 15, 20, and 25. For this case, the resulting skewness might yield a value of 0.00, indicating a symmetric distribution among these three numbers.

Error handling

• DIV/0!: Occurs when there are fewer than three numbers provided for calculation. Ensure you have enough data points.
• VALUE!: Indicates that one or more of the inputs are non-numeric values. Check that all provided values are numbers.
• NUM!: This can happen if the function is provided with an inappropriate range or if the calculations result in an undefined skewness. Verify that your data range is valid and your inputs reasonable.

Conclusion

The SKEW function is an invaluable asset for any data analyst working in Google Sheets. By accurately measuring the skewness of a dataset, it allows for deeper insights into the data’s distribution and symmetry, thereby enhancing analytical capabilities. Understanding skewness, as represented by this function, can ultimately lead to more informed decision-making and data interpretation.