Summary  
This chapter covers detecting and handling missing values in tabular data by filtering nulls, imputing with average values, applying forward-fill or backward-fill methods, and replacing specific incorrect entries.

General domain of usage  
Data preprocessing

Learn how to **handle missing data** in a dataset using Power Query. Missing values can cause **incomplete** or **inaccurate** analysis, making it essential to address them effectively. You will explore how to identify these gaps and replace them with appropriate values using techniques such as **mean**, **median**, or **mode** imputation. Additionally, you will discover how to apply **forward-fill** and **backward-fill** methods to maintain data consistency.

What is the primary reason for handling missing data in Power BI?


What method can you use to replace missing values in Power Query?

Handling Missing Values

1. What is the primary reason for handling missing data in Power BI?

2. What method can you use to replace missing values in Power Query?