PivotTables are a powerful tool for data analysis, but they themselves don't directly identify outliers or anomalies. However, they can be used in conjunction with other techniques to help you spot them. Here's how:

1. Summarize your data: Create a PivotTable summarizing your data by the field you suspect might contain outliers (e.g., sales amount by product).

2. Analyze summary statistics: Look at the summary statistics provided by the PivotTable, such as Sum, Average, Minimum, and Maximum. Values significantly higher or lower than the average might be potential outliers.

3. Calculate quartiles: You can't directly calculate quartiles within a PivotTable, but you can use Excel functions like QUARTILE.ILE or PERCENTILE outside the PivotTable to determine the Interquartile Range (IQR) for your data.

4. Identify outliers based on IQR: Once you have the IQR, calculate the upper and lower bounds (Q1 - 1.5 * IQR and Q3 + 1.5 * IQR). Values falling outside these bounds are possible outliers.

5. Filter and Analyze: Use the PivotTable filters to isolate rows where the value falls outside the calculated bounds. Further investigate these entries to determine if they are genuine outliers or data entry errors.

6. Conditional Formatting: You can use conditional formatting within the PivotTable to highlight cells with values outside a certain range, visually indicating potential outliers.

Additional Techniques:

* Show Values: In the PivotTable value area, right-click and choose "Show Values" > "Difference from Average" to see how much each value deviates from the average.
* Create Charts: PivotTables can generate charts. Look for outliers in scatter plots or box plots where they might be visually distinct.

Remember: These techniques help identify potential outliers, but further investigation is needed to confirm their validity and understand the cause. There might be legitimate reasons for a data point to deviate from the norm.