Daily DAX : Day 371 CHISO.INV.RT

Power BI DAX Function: CHISQ.INV.RT

What is CHISQ.INV.RT?

The CHISQ.INV.RT function in DAX (Data Analysis Expressions) is a statistical function used in Power BI, Power Pivot, and Analysis Services. It calculates the inverse of the right-tailed chi-squared probability distribution. In simple terms:

• Given a probability (between 0 and 1) and degrees of freedom, it returns the critical value (x) such that the area under the chi-squared distribution curve to the right of x equals the given probability.
• This is the inverse of CHISQ.DIST.RT: If CHISQ.DIST.RT(x, deg_freedom) = probability, then CHISQ.INV.RT(probability, deg_freedom) = x.

Syntax

CHISQ.INV.RT(probability, deg_freedom)
  

Parameters

Parameter	Description	Data Type
probability	The right-tailed probability (0 < probability < 1). Represents the significance level (e.g., 0.05 for 95% confidence).	Decimal Number
deg_freedom	Degrees of freedom (must be > 0, typically an integer like (rows-1) * (columns-1) in contingency tables).	Integer

Return Value

A decimal number representing the critical chi-squared value.

Errors

• #NUM!: If probability ≤ 0, probability ≥ 1, or deg_freedom ≤ 0.
• #VALUE!: If either argument is non-numeric.

Example Usage

Suppose you want the critical value for a chi-squared test at 5% significance (probability = 0.05) with 1 degree of freedom:

Critical Value = CHISQ.INV.RT(0.05, 1)
  

Result: Approximately 3.841 (the x where the right-tail area is 0.05).

Use Case: Chi-Squared Test for Independence

The chi-squared test checks if there's a significant association between two categorical variables (e.g., treatment type and patient improvement in a clinical trial). CHISQ.INV.RT finds the critical threshold to decide if your test statistic rejects the null hypothesis (no association).

1. Calculate Chi-Squared Statistic: From observed vs. expected frequencies in a contingency table.
2. Get Critical Value: Use CHISQ.INV.RT(alpha, df), where alpha = significance level (e.g., 0.05), df = degrees of freedom.
3. Compare: If statistic > critical value, reject null hypothesis (significant association exists).

Real-World Scenario: In a Power BI report analyzing sales data, test if "Region" and "Product Category" sales are independent. Compute df = (regions-1) * (categories-1), then use CHISQ.INV.RT to visualize critical values in a dashboard for quick hypothesis testing.

This function is iterative for precision and works in row or filter contexts in DAX measures.