The T.DIST.RT function in Google Sheets is a statistical tool designed to compute the right-tailed probability of the Student’s t-distribution. This function is particularly useful in hypothesis testing, enabling users to determine the likelihood of observing a test statistic that is equal to or greater than a specific value under the assumption of a null hypothesis. By leveraging the properties of the t-distribution, the T.DIST.RT function provides valuable insights into the behavior of sample means in relation to a population mean.

Syntax

T.DIST.RT(x, degrees_freedom)

• x: The numeric value at which you want to evaluate the distribution.
• degrees_freedom: The degrees of freedom corresponding to the t-distribution, typically calculated as the sample size minus one.

Example #1

T.DIST.RT(2.5, 10)

This example calculates the right-tailed probability for a t-value of 2.5 with 10 degrees of freedom. The result could be approximately 0.0165, indicating a 1.65% chance of observing a t-value greater than 2.5, which may affect the decision in a hypothesis test.

Example #2

T.DIST.RT(1.2, 5)

In this instance, the function evaluates the right-tail probability for a t-value of 1.2 with 5 degrees of freedom. The output might be around 0.1362, suggesting that there is a 13.62% probability of encountering a t-value greater than 1.2.

Example #3

T.DIST.RT(-1.5, 20)

This call computes the right-tailed probability for a t-value of -1.5 with 20 degrees of freedom. The result here would be approximately 0.9357, indicating that a t-value below -1.5 is quite likely given the degrees of freedom.

Error handling

• NUM!: This error occurs when the degrees_freedom value is less than 1, which is not valid for a t-distribution.
• VALUE!: This error appears if the x or degrees_freedom parameters contain non-numeric values.

Conclusion

In summary, the T.DIST.RT function is an essential tool for statisticians and researchers relying on the Student’s t-distribution. By providing a means to calculate the right-tailed probabilities, it aids in making informed decisions in hypothesis testing. Understanding how to utilize this function effectively is critical for accurate statistical analysis.