This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
   --> Create model calculations by using DAX
      --> Use Basic Statistical Functions

Note that there are 10 practice questions (with answers and explanations) at the end of each topic. Also, there are 2 practice tests with 60 questions each available on the hub below all the exam topics.

Exam Context

Basic statistical functions are commonly tested in PL-300 as part of descriptive analytics, quality checks, and simple business insights. While not as complex as time intelligence, Microsoft expects candidates to confidently apply these functions correctly and appropriately in measures.

What Are Basic Statistical Functions in DAX?

Basic statistical functions calculate summary statistics over a dataset, such as:

• Average (mean)
• Minimum and maximum
• Count and distinct count
• Variance and standard deviation
• Median

These functions are typically used in measures, evaluated dynamically based on filter context.

Commonly Tested Statistical Functions

AVERAGE

Average Sales =
AVERAGE(Sales[SalesAmount])

Calculates the arithmetic mean of a numeric column.

MIN and MAX

Minimum Sale = MIN(Sales[SalesAmount])
Maximum Sale = MAX(Sales[SalesAmount])

Used to identify ranges, thresholds, and outliers.

COUNT vs COUNTA vs COUNTROWS

Transaction Count = COUNT(Sales[SalesAmount])
Row Count = COUNTROWS(Sales)

DISTINCTCOUNT

Unique Customers =
DISTINCTCOUNT(Sales[CustomerID])

Frequently tested for:

• Customer counts
• Product counts
• Unique identifiers

MEDIAN

Median Sales =
MEDIAN(Sales[SalesAmount])

Useful when data contains outliers, as the median is less sensitive than the average.

Variance and Standard Deviation

VAR.P and VAR.S

Sales Variance = VAR.P(Sales[SalesAmount])

• VAR.P → Population variance
• VAR.S → Sample variance

STDEV.P and STDEV.S

Sales Std Dev = STDEV.P(Sales[SalesAmount])

Used to measure dispersion and variability in data.

⚠️ Exam Tip:
Know the difference between population and sample functions, even if not deeply mathematical.

Statistical Functions with CALCULATE

Statistical measures often require modified filter context:

Average Sales for Bikes =
CALCULATE(
    AVERAGE(Sales[SalesAmount]),
    Product[Category] = "Bikes"
)

This pattern is commonly used in scenario-based exam questions.

Statistical Functions vs Iterators

Example:

Average Sales Per Order =
AVERAGEX(
    VALUES(Sales[OrderID]),
    [Total Sales]
)

👉 Exam Insight:
Use iterator versions when calculations depend on row-level logic.

Common Mistakes (Often Tested)

• Using COUNT instead of DISTINCTCOUNT
• Using averages when median is more appropriate
• Creating statistical calculations as calculated columns
• Forgetting filter context impacts results
• Misunderstanding COUNT vs COUNTROWS

Best Practices for PL-300 Candidates

• Use measures, not calculated columns
• Choose the correct counting function
• Use CALCULATE to apply business filters
• Prefer MEDIAN for skewed data
• Validate results at different filter levels

How This Appears on the Exam

Expect questions that:

• Ask which function returns a specific statistic
• Compare COUNT, COUNTA, and COUNTROWS
• Require choosing the correct aggregation
• Identify incorrect use of statistical functions
• Test understanding of filter context effects

Key Takeaways

• Basic statistical functions are foundational DAX tools
• Most are evaluated dynamically as measures
• Filter context strongly affects results
• Correct function choice is critical on the exam
• Frequently combined with CALCULATE

Practice Questions

Go to the Practice Exam Questions for this topic.