Learn on PengiReveal Math, Course 1Module 10: Statistical Measures and Displays

10-6 Outliers

In this Grade 6 lesson from Reveal Math Course 1, Module 10, students learn how to identify outliers in a data set using the interquartile range (IQR), specifically by calculating upper and lower limits based on Q1, Q3, and 1.5 times the IQR. Students then explore how the presence of an outlier affects measures of center such as the mean and median, and measures of variation such as the MAD and IQR. By comparing calculations with and without an outlier, students determine which measure of center best represents a data set depending on whether an outlier is present.

Section 1

Identifying Outliers (The 1.5 x IQR Rule)

Property

An outlier is an item in a data set that is much larger or much smaller than the other items in the set. The presence of an outlier can have a misleading effect on the measures of central tendency and dispersion.
To mathematically prove a number is an outlier, we use the IQR method to set up invisible boundary fences:

  • Lower boundary = Q1 - (1.5 × IQR)
  • Upper boundary = Q3 + (1.5 × IQR)

Any data value less than the lower boundary or greater than the upper boundary is officially considered an outlier.

Examples

Lesson overview

Expand to review the lesson summary and core properties.

Expand

Section 1

Identifying Outliers (The 1.5 x IQR Rule)

Property

An outlier is an item in a data set that is much larger or much smaller than the other items in the set. The presence of an outlier can have a misleading effect on the measures of central tendency and dispersion.
To mathematically prove a number is an outlier, we use the IQR method to set up invisible boundary fences:

  • Lower boundary = Q1 - (1.5 × IQR)
  • Upper boundary = Q3 + (1.5 × IQR)

Any data value less than the lower boundary or greater than the upper boundary is officially considered an outlier.

Examples