Identifies which values in a dataset qualify as statistical outliers, using the standard 1.5×IQR rule.
How it works
The interquartile range sets upper and lower “fences” at 1.5 times the IQR beyond Q1 and Q3; any value outside those fences is flagged as an outlier.
What this does not include
This does not include just reporting the IQR itself — for that number alone, use this site’s IQR calculator instead.
How to use this calculator
- Enter your dataset as comma-separated numbers.
A worked example
Dataset 2, 4, 4, 4, 5, 5, 7, 9, 50: Q1 and Q3 set fences at −2 (lower) and 14 (upper). The value 50 falls outside the upper fence, so it’s flagged as the outlier.
Dataset 1, 2, 3, 4, 5 has no values outside its fences — the result is None.
What the variables mean
| Term | Meaning |
|---|---|
| Lower fence | Q1 − 1.5 × IQR |
| Upper fence | Q3 + 1.5 × IQR |
| Outlier | Any value falling outside either fence |
Edge cases worth knowing
A flagged outlier isn’t automatically a mistake. It’s a statistically unusual value relative to the rest of the dataset — that could reflect a genuine extreme case, a data-entry error, or natural variability worth a closer look.
“None” is a completely valid, common result — most tightly clustered datasets have no values extreme enough to cross either fence.
Frequently asked questions
Why 1.5 times the IQR specifically?
It’s a standard convention (commonly attributed to statistician John Tukey) that balances catching genuine outliers without flagging too many ordinary values in a roughly normal dataset.
Does a flagged outlier mean the data point is wrong?
Not necessarily — it means the value is unusual relative to the rest of the dataset, which could reflect a genuine extreme case, a measurement error, or natural variability worth investigating further.
What if my dataset has no outliers?
The calculator reports “None” — not every dataset has values that fall outside the 1.5×IQR fences.
