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Outlier Detector

Find potential outliers in selected numeric CSV columns and export detected rows for review.

Local browser-side processing

Your selected file is processed locally in your browser. ScholarTool does not upload the file to a third-party calculation or data-analysis API.

1. Select and load CSV

Selecting a file only records file details. Click Load CSV to parse and preview input.

Choose CSV fileAccepted: .csv, text/csv, and compatible plain-text CSV files. Limit: 25.0 MB.

Drag and drop is optional; the normal file chooser is the primary accessible control.

DelimiterHeader rowSkip empty linesCommon missing tokens

Custom missing tokensTrim for missingCase-sensitive

2. Configure and run tool

Results, visuals, downloads, and copy actions remain hidden until Detect Outliers succeeds.

Load a CSV file to enable processing options.

What This Tool Does

The Outlier Detector runs only after a CSV is loaded and you click Detect Outliers. It supports IQR fences, Z-scores, and modified Z-scores with clear warnings for constant or low-variation columns.

Outlier detection method

IQR flags values outside Q1/Q3 plus or minus the configured IQR multiplier. Z-score uses sample mean and sample standard deviation. Modified Z-score uses median and MAD.

Worked Example

Load a measurement CSV, select pressure and temperature columns, run IQR detection at 1.5, then inspect flagged source rows before deciding whether values are errors.

Common Mistakes

Deleting every outlier without checking whether it is a valid extreme event.
Using Z-score on small or heavily skewed samples without reviewing assumptions.
Ignoring constant-column warnings where outlier scores are undefined.

Limitations

Outlier detection labels potential outliers; it does not prove that a value is wrong.
Z-score methods can be misleading for non-normal or very small samples.
Multivariate outlier detection is not included in this phase.

References

NIST outlier guidance: NIST Engineering Statistics Handbook guidance on outlier detection and modified Z-scores.
RFC 4180: Common Format and MIME Type for Comma-Separated Values (CSV) Files.
Papa Parse documentation: Official Papa Parse CSV parsing documentation for browser and JavaScript workflows.

What is the IQR rule?

The IQR rule flags values below Q1 minus a multiplier times IQR or above Q3 plus that multiplier times IQR.

What is a Z-score?

A Z-score measures how many sample standard deviations a value is from the sample mean.

What is a modified Z-score?

A modified Z-score uses the median and median absolute deviation, making it more robust to extreme values.

Is every outlier an error?

No. Outliers may be valid extreme observations, data-entry issues, or evidence that a different model is needed.

Which method should be used?

IQR is a practical default. Modified Z-score is useful for robust screening. Z-score is most appropriate when normality is plausible.

Are CSV files uploaded to ScholarTool?

No. The selected CSV file is read and processed locally in your browser. ScholarTool does not upload the file to a third-party calculation or data-analysis API.

Can the tool handle large CSV files?

The browser-local workflow enforces a 25 MB file limit, 200,000 parsed-row limit, 250-column limit, bounded previews, and bounded result tables to avoid freezing the page.

Engineering disclaimer

This tool is intended for educational, estimation, and preliminary data-preparation use. Always verify critical data-processing decisions with validated workflows and qualified professional judgment before using results in real research, compliance, or engineering decisions.

Need Professional Support?

ScholarTool is powered by ScholarEase Consultancy Services LLP. Professional engineering, simulation, research, and technical documentation support is available through the connected services website.

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Last reviewed: 2026-07-02

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What This Tool Does

The Outlier Detector runs only after a CSV is loaded and you click Detect Outliers. It supports IQR fences, Z-scores, and modified Z-scores with clear warnings for constant or low-variation columns.

Outlier detection method

IQR flags values outside Q1/Q3 plus or minus the configured IQR multiplier. Z-score uses sample mean and sample standard deviation. Modified Z-score uses median and MAD.

Outlier Detector

Local browser-side processing

1. Select and load CSV

2. Configure and run tool

What This Tool Does

Outlier detection method

Worked Example

Common Mistakes

Limitations

References

Related Tools

Engineering disclaimer

Need Professional Support?

Outlier Detector

Local browser-side processing

1. Select and load CSV

2. Configure and run tool

What This Tool Does

Outlier detection method

Worked Example

Common Mistakes

Limitations

References

Related Tools

Engineering disclaimer

Need Professional Support?