Outliers
Values beyond 1.5 IQR from the quartiles.
Key idea
Values beyond 1.5 IQR from the quartiles.
In depth
An outlier is a value far from the rest. A common rule flags values below Q1 − 1.5·IQR or above Q3 + 1.5·IQR. Investigate before removing: it could be an error or the most interesting data point.
Worked example
Q1 = 20, Q3 = 30, IQR = 10: fences at 5 and 45. A value of 60 is an outlier.
Real-world uses
- Spotting a faulty sensor reading or an unusual sale that should be checked before analysis
Common mistake
Deleting outliers automatically.
How to learn this
- Read the key idea and say it in your own words.
- Work through the worked example, then try a similar problem.
- Use the interactive lesson’s visual and mini-quiz to check the pattern.
Frequently asked questions
What is Outliers?
Values beyond 1.5 IQR from the quartiles. It is a topic at the advanced level in Probability & Statistics.
Where is Outliers used in real life?
Real-world examples: Spotting a faulty sensor reading or an unusual sale that should be checked before analysis.
What should I learn before Outliers?
Make sure you are comfortable with Box Plots, the previous topic in Probability & Statistics.
What is a common mistake with Outliers?
Deleting outliers automatically.
How can I practise Outliers?
Open the interactive lesson, play with the visual, then answer the three-question mini-quiz. Getting two right marks the topic complete.
What comes after Outliers?
Move on to Variance next.
Related advanced topics in Probability & Statistics
- Quartiles and IQR – Quartiles split data into four parts; IQR = Q3 − Q1.
- Box Plots – Five-number summary visualised.
- Variance – Average squared deviation from the mean.
- Standard Deviation – Square root of variance; typical spread.
- Sample vs Population Variance – Divide by n−1 for sample variance.
- Weighted Mean – Average where values have different importance.