Outlier Hunter

Can you spot the outliers hiding in business data? Click on suspicious data points and test your statistical intuition.

How to Play

  • 1Each round shows a dataset from a real-world business scenario with 12-15 sorted values.
  • 2Click on any data points you believe are outliers to flag them.
  • 3Submit your selections and see how they compare to the IQR method.
  • 4Score points for correctly identifying all outliers without false positives.
The IQR Method:
Q1 = 25th percentile  |  Q3 = 75th percentile
IQR = Q3 − Q1
Lower Fence = Q1 − 1.5 × IQR
Upper Fence = Q3 + 1.5 × IQR
Any value below the lower fence or above the upper fence is an outlier.
Round 1 of 10 Score: 0 / 100

Click on any values you think are outliers, then submit.

Game Complete!

0 / 100
A

The IQR Method Recap

The Interquartile Range (IQR) method is one of the most common ways to detect outliers in data. It uses the spread of the middle 50% of data to define boundaries. Any value beyond 1.5 times the IQR from the quartiles is considered an outlier.

Common Mistakes

  • Flagging values that seem large but fall within the fences — always compute the math.
  • Forgetting that outliers can appear on both ends (low and high) of a dataset.
  • Confusing “unusual” with “outlier” — the IQR method has a precise definition.
  • Not considering that small datasets can have wide IQR ranges, making outlier detection harder.