The Google Sheets function NORM.INV is a statistical tool used to calculate the inverse of the normal distribution for a specified mean and standard deviation. This function is essential for data analysis, particularly when dealing with probabilities associated with normally distributed variables. It enables users to determine a value corresponding to a given cumulative probability, making it invaluable in fields such as finance and research.

Syntax

NORM.INV(probability, mean, standard_deviation)

• probability: A numeric value representing the cumulative probability for which you want to find the corresponding x-value.
• mean: The mean of the normal distribution.
• standard_deviation: The standard deviation of the normal distribution.

Example #1

NORM.INV(0.95, 100, 15)

This function call calculates the value at which 95% of the data falls below in a normal distribution with a mean of 100 and a standard deviation of 15. The result may be approximately 124.74.

Example #2

NORM.INV(0.5, 50, 10)

Here, this function retrieves the median value (50% probability) of a normal distribution with a mean of 50 and standard deviation of 10, yielding a result of 50.

Example #3

NORM.INV(0.01, 30, 5)

This calculates the value at which only 1% of the data falls below in a normal distribution with a mean of 30 and a standard deviation of 5, resulting in a value of approximately 19.84.

Error handling

• NUM!: This error occurs if the probability is less than 0 or greater than 1, or if the standard_deviation is non-positive. Ensure that your inputs fall within valid ranges.
• VALUE!: This results when a non-numeric value is provided for any of the parameters. Check to make sure you’re using numeric inputs.

Conclusion

The NORM.INV function in Google Sheets is a vital tool for conducting statistical analysis, particularly when working with normal distributions. By understanding its syntax and application, users can efficiently determine critical values based on probabilities, enhancing data interpretation and decision-making processes.