1. Identify and remove outliers: Identify outliers by examining the distribution of the data or using statistical methods like the z-score or modified z-score. Once identified, you can exclude them from the calculation of the average by using a WHERE clause to filter them out. For example:

   SELECT AVG(column_name)
   FROM table_name
   WHERE column_name NOT BETWEEN (Q1 - 1.5 * IQR) AND (Q3 + 1.5 * IQR);
   

   Here, Q1 and Q3 represent the first and third quartiles, respectively, and IQR is the interquartile range.

2. Winsorization: Winsorization is a method where outliers are replaced with values from the nearest non-outlier data points. This approach mitigates the effect of outliers without removing them completely. The modified dataset can then be used to calculate the average. Here’s an example:

   SELECT AVG(CASE
                 WHEN column_name < lower_bound THEN lower_bound
                 WHEN column_name > upper_bound THEN upper_bound
                 ELSE column_name
             END)
   FROM table_name;
   

   In this example, lower_bound and upper_bound represent the threshold values that define the range within which the outliers are replaced.

3. Robust estimators: Instead of using the AVG function, you can use robust estimators like the median or trimmed mean to calculate the average. These estimators are less sensitive to outliers and provide a more accurate representation of the central tendency of the data.

   SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY column_name) 
   FROM table_name;
   

   Using the PERCENTILE_CONT function with a value of 0.5 calculates the median.

By implementing these strategies, you can handle outliers and abnormal values effectively when calculating the average in SQL. Choose the method that best suits your data and analytical requirements. #SQL #OutlierHandling