The T.DIST function in Google Sheets is a valuable statistical tool used to calculate the probability of a right-tailed Student’s t-distribution for a specific value, x. This function is particularly useful in hypothesis testing and in scenarios where determining probabilities from sample data is essential.

Syntax

T.DIST(x, degrees_freedom, cumulative)

• x: The numeric value for which you want the distribution result.
• degrees_freedom: Specifies the degrees of freedom, typically calculated as the sample size minus one.
• cumulative: A logical value that determines the form of the function. If TRUE, T.DIST returns the cumulative distribution function; if FALSE, it returns the probability density function.

Example #1

T.DIST(2, 10, TRUE)

This function calculates the cumulative probability of a right-tailed t-distribution for a value of 2 with 10 degrees of freedom. The result is approximately 0.941.

Example #2

T.DIST(1.5, 15, FALSE)

Here, the function computes the probability density for a value of 1.5 with 15 degrees of freedom. The result yields about 0.134.

Example #3

T.DIST(0, 20, TRUE)

This instance evaluates the cumulative probability for a value of 0 with 20 degrees of freedom, resulting in exactly 0.5, as it represents the midpoint of the distribution.

Error handling

• NUM! This error arises when the “degrees_freedom” is not a positive number. Ensure that you’re entering a valid positive integer.
• VALUE! This error occurs if any of the provided arguments are of the incorrect type, such as providing text instead of a number. Make sure all inputs are numeric.
• REF! This error is displayed when one of the referenced cells for the parameters is invalid. Check the cell references to ensure they are correct.

Conclusion

In summary, the T.DIST function is an important analytical tool in Google Sheets, facilitating the understanding of t-distributions through its straightforward parameters. It aids researchers and statisticians in analyzing data by providing essential probabilities derived from sample datasets. Proper understanding of its syntax and handling of potential errors can enhance data analysis accuracy and reliability.