Data Management & Editing · TOOL GUIDE

Outlier Detection

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Understand the method. Use it with confidence.

Detect outliers using IQR or Z-score methods. Quantly runs the method against your active analysis context and returns structured results for review and interpretation.

01

When to use it

Use this tool while preparing an analysis-ready dataset or correcting a known data issue.
Use it only when the selected variables and study design match the method's requirements.

02

How to use it

  1. Open the tool from the Quantly Tools panel.
  2. Confirm the active dataset and analysis session.
  3. Set Select Numeric Columns (Required): Choose one or more numeric columns to check for outliers.
  4. Set Detection Method (Required): The method to use for outlier detection.
  5. Set IQR Multiplier (Optional): Multiplier for the IQR score to identify outliers.
  6. Set Z-score Threshold (Optional): Number of standard deviations from the mean to be considered an outlier.
  7. Review optional or advanced settings and keep defaults unless your design requires a change.
  8. Run the tool, inspect warnings and diagnostics, then save or export the result with its assumptions.
03

How to interpret results

Confirm the affected rows and columns before saving. Review the output dataset and warning counts; transformations change data rather than proving a statistical conclusion.