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?

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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?