Outlier & Distribution Analyzer
Review quartiles, interquartile range, possible outliers and a simple numeric histogram.
Outlier & Distribution Analyzer
Review quartiles, interquartile range, possible outliers and a simple numeric histogram.
Distribution histogram
Flagged observations
The interquartile-range rule is a screening convention, not proof that a value is wrong. Check units, data-entry issues, population differences and legitimate rare events before excluding anything.
These are practical analysis aids, not a substitute for checking source data, definitions, units, and sampling choices.
A practical note on Outlier & Distribution Analyzer
The question worth answering: This page is most useful when you focus on distinguishing an error from a rare but real event. Reviewing a few unusually large orders before calculating average order value. Start by recording the input source, the date or period covered, and any assumption you had to supply yourself. That makes the result easier to reproduce and helps another person understand what the output actually says.
How to use the result responsibly
Read the output alongside the inputs rather than as a stand-alone verdict. Change one assumption at a time and observe whether the conclusion changes materially. If the tool accepts a range, test more than one plausible case; if it accepts a file or pasted data, preserve an untouched copy of the original. Where the result will support a published claim or an important decision, keep a brief record of the method and source date.
A mistake specific to this kind of task
An outlier is not automatically a bad record; deleting a genuine extreme observation may hide the most important business event. This is why the output should be described as an estimate, diagnostic or comparison where appropriate—not as a guarantee. Avoid mixing currencies, periods, definitions or data sources merely because their labels look similar.
Independent check before acting
Check source records and use median or robust summaries alongside the mean; document any exclusion rule before applying it. If the result conflicts with a statement, official record, provider terms or the original dataset, investigate the difference before relying on it. The tool is intended to make a task clearer, not to replace the source system or professional advice when the consequences are material.
Reader takeaway: Keep the assumptions visible, verify the underlying evidence and use this result as one part of a reasoned decision.