Outlier Calculator
Enter data values to compute quartiles, IQR, and outlier fences. Customize the multiplier to adjust sensitivity.
1.5 identifies mild outliers, 3.0 flags extreme outliers.
Q1 • IQR • Q3
Q1: 18.7500 • IQR: 13.5000 • Q3: 32.2500
Lower / Upper fence
[-1.5000, 52.5000]
Outliers
100
How to Use This Calculator
- Enter a dataset of numeric values.
- Adjust the multiplier to control outlier sensitivity.
- Review quartiles, IQR, and outlier bounds.
- Flag points outside the fences as potential outliers.
Formula
IQR = Q3 − Q1
Lower fence = Q1 − factor × IQR
Upper fence = Q3 + factor × IQR
Points outside the fences are considered outliers. The default factor of 1.5 reflects Tukey's rule.
Full Description
Outlier detection helps identify anomalous observations for further investigation. The IQR method is robust to skewness and provides a quick rule-of-thumb for flagging extreme values without assuming normality.
Use domain expertise to determine whether flagged points are errors, rare events, or important signals.
Frequently Asked Questions
Can I use different multipliers?
Yes. 1.5 detects mild outliers; 3.0 highlights extreme outliers. Adjust based on context.
Does this method assume normal distribution?
No. It is a nonparametric method based on quantiles, making it robust to skewness.
What if my dataset is very small?
With few points, quartile estimates may be unstable, so interpret results cautiously.
Are all outliers errors?
Not necessarily. Outliers may be valid observations signaling important events or heterogeneity.