The CHISQ.DIST.RT function in Google Sheets is a powerful statistical tool designed to compute the right-tailed chi-squared distribution. This function is primarily employed in hypothesis testing, particularly in scenarios where one needs to ascertain the significance of observed data compared to the expected outcome. It provides valuable insights, especially in fields like research, quality control, and social sciences.

Syntax

CHISQ.DIST.RT(x, degrees_freedom) 

• x: This is the value at which you want to evaluate the chi-squared distribution. It must be non-negative.
• degrees_freedom: This signifies the degrees of freedom associated with the distribution, which must be a positive integer.

Example #1

=CHISQ.DIST.RT(5, 2) 

This function calculates the right-tailed chi-squared distribution for a value of 5 with 2 degrees of freedom. The result is approximately 0.086. This indicates a moderate level of significance in a hypothesis test where the cutoff is typically set at 0.05.

Example #2

=CHISQ.DIST.RT(10, 5) 

Here, the function evaluates the right-tailed chi-squared distribution at a value of 10 with 5 degrees of freedom. The result is approximately 0.071. This suggests a significant p-value indicating that the observed results may indeed deviate from the expected outcomes.

Example #3

=CHISQ.DIST.RT(3, 1) 

In this case, the function assesses the right-tailed chi-squared distribution for a value of 3 with 1 degree of freedom, yielding approximately 0.049. This value is near the typical significance threshold, pointing towards the possibility of a meaningful result.

Error handling

• NUM!: This error occurs if the x value is negative or if the degrees_freedom is not a positive integer, indicating invalid input parameters.
• VALUE!: This error arises when the provided degrees_freedom is not recognized as a number, suggesting that you need to check the cell reference or ensure it is a numerical value.

Conclusion

In summary, the CHISQ.DIST.RT function is a crucial tool in statistical analysis, allowing users to assess the right-tailed chi-squared distribution effectively. By understanding its syntax and applications, users can leverage this function to conduct hypothesis testing and draw meaningful conclusions from their data.