Outlier Calculator

Compare IQR, z-score, and modified z-score potential-outlier flags, then inspect method agreement before deciding what deserves review.

IQR fences1.5× review + 3× extreme fence

Z-scoremean + standard deviation view

Modified z-scoremedian + MAD robust view

Method agreementcompare flags before deciding

Dataset

Quartile method

—Potential 1.5×IQR outliers

—Q1

—Q3

—IQR

—Fences

The 1.5×IQR rule flags potential outliers for review; it does not prove that a value is erroneous or should be removed.

Outlier detection workbench

Compare three methods instead of trusting one flag

Reuse the dataset and quartile method above, then compare IQR fences, conventional z-scores, and median/MAD modified z-scores observation by observation.

Flag for review · never auto-delete

IQR multiplier|z| cutoff|modified z| cutoffStandard deviationLocal file

—n

—Mean

—Median

—Standard deviation

—MAD

—IQR flags

—Z-score flags

—Modified-z flags

—2+ method agreement

IQR box-plot preview

Observation-by-observation comparison

#	Value	IQR	z-score	Modified z	Agreement

Impact if consensus flags were excluded

—

Copyable outlier audit

Comparing the current dataset…

Interpretation boundary. A potential outlier is a value to investigate, not proof of a data error. IQR, z-score, and modified z-score answer different questions and can disagree. Standard z-scores can be especially misleading in small samples because the mean and SD are themselves affected by extreme values. Do not remove observations automatically; check measurement, data-entry, sampling and domain context first.

Core describe → test → interpret workflows

Use descriptive context first, choose the method that matches the design, then keep inputs, settings and result evidence reproducible instead of copying a p-value without its analysis recipe.

Start: Statistics Calculator Choose a statistical test

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Interpret statistical results in context

Use plots and descriptive summaries before formal tests. Check the sampling/design assumptions that matter for the chosen method, report effect size and uncertainty where available, and avoid treating a threshold such as p < .05 as proof of importance, causation, or truth. Browser calculations are educational/planning utilities, not domain-specific professional advice.

How to use this Outlier Calculator

Paste a dataset, choose a quartile method, and calculate Q1, Q3, IQR, lower and upper fences, and values outside those fences.