Outlier Fence Calculator

Frequently Asked Questions

What is an outlier?

Value that lies far from most other values. Unusually high or low compared to the rest of data. Can indicate errors, special cases, or true anomalies.

What is the 1.5×IQR rule?

Standard method for identifying outliers. Calculate fences: Lower = Q1 - 1.5×IQR, Upper = Q3 + 1.5×IQR. Values outside fences are outliers.

How do I calculate the lower fence?

Lower fence = Q1 - 1.5×IQR. Values below this are potential outliers. The 1.5 factor is standard statistical convention.

How do I calculate the upper fence?

Upper fence = Q3 + 1.5×IQR. Values above this are potential outliers. Symmetric formula around quartiles.

Why use 1.5?

The 1.5 constant was chosen by statisticians to balance sensitivity. Catches most real outliers while avoiding false positives. Empirically proven for normal data.

What if a value is exactly on the fence?

Values exactly on the fence line are not considered outliers. Only values strictly beyond the fence count. > Upper or < Lower = outlier.

Can I modify the 1.5 factor?

Yes. 1.5×IQR is standard. Some use 2×IQR or 3×IQR for more conservative outlier detection. Smaller multiplier = more outliers detected.

How do outlier fences relate to the 68-95-99.7 rule?

For normal data, 1.5×IQR roughly corresponds to ~3 standard deviations (99.7% of data). Not exact but similar concept.

What do I do if I find outliers?

Investigate. Are they data entry errors? Real unusual cases? Domain knowledge helps. Don't automatically delete—understand first.

Do I need to detect all outliers?

Depends on analysis. Some use fences for box plots (visual). Others use 2×IQR for more conservative approach. Choose based on purpose.