Daily DAX : Day 391 NORM.S.DIST

NORM.S.DIST Function in Power BI DAX

Function Syntax

NORM.S.DIST(z, cumulative)

Parameters

Parameter Description
z The value for which you want the distribution (z-score). Must be a numeric value.
cumulative A logical value (TRUE/FALSE):
TRUE = Returns the cumulative distribution function (CDF)
FALSE = Returns the probability density function (PDF)

What It Returns

  • If cumulative = TRUE: Probability that a standard normal random variable is ≤ z
  • If cumulative = FALSE: Height of the standard normal curve at point z

Example 1: Cumulative Probability (CDF)

NORM.S.DIST(1.96, TRUE)

Result: ≈ 0.975

Meaning: 97.5% of the data in a standard normal distribution lies below a z-score of 1.96.

Example 2: Probability Density (PDF)

NORM.S.DIST(0, FALSE)

Result: ≈ 0.3989

Meaning: The peak of the standard normal curve at z = 0 is about 0.4.

Use Cases in Power BI

  • Statistical Analysis: Calculate p-values or confidence intervals from z-scores.
  • Quality Control: Determine how many standard deviations a measurement is from the mean (e.g., Six Sigma dashboards).
  • Risk Assessment: Model probabilities in financial or operational risk metrics using z-scores.
  • Dynamic Thresholds: Flag outliers — e.g., show values where NORM.S.DIST(z, TRUE) > 0.95 as "top 5% performers".

Note: This function assumes a standard normal distribution (mean = 0, standard deviation = 1). For non-standard normal distributions, use NORM.DIST instead.

Practical Example in Power BI

Suppose you have a column of z-scores in a table called SalesData:

P-Value = NORM.S.DIST([Z-Score], TRUE)
Probability Density = NORM.S.DIST([Z-Score], FALSE)
        

You can create a measure to identify extreme values:

Is Outlier (95% Confidence) = 
IF(
    NORM.S.DIST(ABS([Z-Score]), TRUE) > 0.975,
    "Yes",
    "No"
)
        

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