Review unusual observations without losing context
Paste a CSV with a header row, select a numeric column and calculate. Text IDs and other fields stay attached to each observation. Switch the detection rule to compare how assumptions change the flagged rows. The chart highlights flagged points in red; the export keeps both flagged and unflagged records.
Understand the default example
The sample contains nine values between 10 and 17 and one value of 80. Linear-interpolated quartiles are 12 and 15.75, producing IQR fences of 6.375 and 21.375. IQR flags 80. The strict population Z-score rule does not flag it because its score remains below 3. This is a useful demonstration of how a large value changes its own reference mean and spread.
Choose the unit of analysis
A single-column rule cannot identify every unusual combination of features. Review comparable populations together and keep units consistent. The JavaScript Isolation Forest guide covers a multivariate alternative. A statistical flag is not a diagnosis of fraud or proof of a data error.
Work through a practical example
How to Find Outliers in a CSV File Without Python explains the input, assumptions and expected output. Continue with IQR vs Z-Score vs Isolation Forest: Compare the Same Dataset to compare methods and interpret results.