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Statistics on data

Outlier detection calculator online

Two rules, applied at once, because they fail in different ways. The IQR rule is robust but conservative; the z-score rule is sensitive but can be blinded by the very outliers it is looking for. Rows caught by both are the strongest candidates for a second look.

Your data never leaves this device

Your data

Paste a table straight from Excel or Google Sheets, or drop a CSV file. Nothing is uploaded anywhere — the whole calculation runs inside this browser tab.

CSV, TSV or plain text. Files saved in windows-1251 are detected and re-read automatically, so Cyrillic headers do not turn into garbage.

Parsing options

Columns

Result

The drawing library is downloaded only when you press the button, and only on this page.

This page has no server side at all. The table you paste is parsed by JavaScript inside your own browser, the chart is drawn on a canvas element on your machine, and the export file is assembled locally. Nothing is uploaded, stored or written to any log. You can disconnect from the network after the page has loaded and everything will still work — which is the simplest way to verify the claim yourself.

Facts and limits of this method

IQR rule Outside Q1 − 1.5·IQR or Q3 + 1.5·IQR
Extreme threshold Outside 3·IQR — flagged separately as extreme
Z-score rule Absolute z above 3, computed with the sample standard deviation
Robustness IQR is unaffected by the outliers themselves; the z-score is not
Output per row Value, z-score and which rule or rules flagged it
Minimum rows 4

When it misleads you

How it is calculated

The IQR rule builds fences at Q1 − 1.5·IQR and Q3 + 1.5·IQR. Because quartiles are positional, the fences do not move when an outlier gets more extreme — a value ten times too large is caught just as reliably as one twice too large.

The z-score rule measures each value in standard deviations from the mean and flags anything beyond three. Both of those ingredients are themselves distorted by outliers, which is exactly the weakness the IQR rule does not share, and the reason both are run rather than one.

A third threshold at 3·IQR marks extreme outliers separately. The distinction is practical: a value just past the fence often turns out to be legitimate, while one past three interquartile ranges is usually either an error or a different kind of event entirely.

Every flagged row is listed with its position in the file so you can go back and look at it. The export contains all rows with their z-scores and a flag column, so the decision about what to do with them stays yours rather than being applied silently.

Questions and answers

Should I delete the outliers?

Not automatically. Deleting values because they are inconvenient is how analyses go wrong. Investigate first: an error gets corrected, a genuine extreme event gets reported, and a mixed population gets split into separate groups.

The two rules disagree. Which do I trust?

Prefer the IQR rule when they conflict, because it is not distorted by the outliers themselves. The z-score rule is more useful as a measure of how extreme a value is than as a detector.

Why did nothing get flagged when I can see an obvious outlier?

Probably masking: with few rows, one extreme value inflates the standard deviation enough to keep its own z-score below 3. Check whether the IQR rule caught it — it usually does.

What counts as too many outliers?

More than about 2–3% of your rows suggests the rules are not detecting anomalies but describing a skewed distribution. Check normality; on skewed data the fences flag a whole tail.

Is my data uploaded?

No. Both rules run entirely in your browser.

The opposite tool

Need it the other way round? Z-score calculator Every value expressed in standard deviations from the mean

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