Difference between STDEV.S and STDEV.P in DAX with Example

🎯 Objective

Show the difference between STDEV.S() and STDEV.P() DAX functions using real-world data from the Sample Superstore dataset (e.g., Orders table).

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📌 Quick Definitions

Function	Meaning	Divisor Used	When to Use
STDEV.S()	Sample Standard Deviation	Divides by n - 1	Use when data is a sample of the population
STDEV.P()	Population Standard Deviation	Divides by n	Use when data represents the entire population

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🗃️ Sample Superstore Table Used

We’ll work with the Orders table, especially the [Sales] column.

Let’s say your table has the following simplified sales data:

Order ID	Sales
A	200
B	300
C	400
D	500
E	600

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Here in this screenshot you can see how it is applied on Sample Superstore Dataset using Power BI

🧪 DAX Example in Power BI

✅ 1. Sample Standard Deviation

Sales STDEV Sample = STDEV.S(Orders[Sales])

Explanation:

• Uses n - 1 divisor (Bessel’s correction)
• Slightly higher standard deviation
• Good for analyzing a subset of all your orders

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✅ 2. Population Standard Deviation

Sales STDEV Population = STDEV.P(Orders[Sales])

Explanation:

• Uses n as divisor
• Smaller than STDEV.S
• Use when your dataset includes all orders ever made

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📊 Comparison Example

Let’s calculate both with this data:

Sales = [200, 300, 400, 500, 600]

Step 1: Mean

Mean = (200 + 300 + 400 + 500 + 600) / 5 = 2000 / 5 = 400

Step 2: Squared Deviations

(200 - 400)^2 = 40000
(300 - 400)^2 = 10000
(400 - 400)^2 =     0
(500 - 400)^2 = 10000
(600 - 400)^2 = 40000
Sum = 100000

STDEV.P:

= SQRT(100000 / 5) = SQRT(20000) ≈ **141.42**

STDEV.S:

= SQRT(100000 / 4) = SQRT(25000) ≈ **158.11**

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✅ Summary

Function	Formula	Result (Example)	Use Case
STDEV.P([Sales])	√(Σ(x – x̄)² / n)	141.42	If entire order history is analyzed
STDEV.S([Sales])	√(Σ(x – x̄)² / (n-1))	158.11	If analyzing a sample of orders (e.g., just 1 month or 1 region)

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💡 Real Insight:

• Use STDEV.S when analyzing performance by Region, Category, or a filtered subset (e.g., last 30 days).
• Use STDEV.P when looking at entire dataset trends, e.g., total sales variability across all time.

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