Outlier Calculator
Detect outliers in your data set using two standard methods: the IQR (interquartile range) method and the z-score method. Enter your values to see which data points fall outside the expected range.
Outliers are data points that differ significantly from the rest of a data set. Identifying them is a critical first step in data analysis because outliers can skew averages, inflate variance, and distort statistical models.
IQR Method (Tukey's Fences):
- Sort the data and find Q1 (25th percentile) and Q3 (75th percentile)
- Calculate IQR = Q3 - Q1
- Lower fence = Q1 - 1.5 x IQR
- Upper fence = Q3 + 1.5 x IQR
- Any value outside the fences is an outlier
This method is robust because it relies on quartiles, which are not heavily influenced by extreme values.
Z-Score Method:
- Calculate the mean and standard deviation
- For each value, compute z = (x - mean) / SD
- Flag values where |z| exceeds the chosen threshold (commonly 2 or 3)
The z-score method works well for roughly symmetric, bell-shaped data but can miss outliers in skewed distributions because the mean and SD are themselves pulled by the outlier.
Which method should you use? The IQR method is generally more robust and works for any data shape. The z-score method is best for approximately normal data. Using both gives you a more complete picture.