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) > 0.95as "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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