Lesson 5

Variability and MAD

Lesson Narrative

In a previous lesson, students computed and interpreted distances of data points from the mean. In this lesson, they take that experience to make sense of the formal idea of mean absolute deviation (MAD). Students learn that the MAD is the average distance of data points from the mean. They use their knowledge of how to calculate and interpret the mean to calculate (MP8) and interpret (MP2) the MAD.

Students also learn that we think of the MAD as a measure of variability or a measure of spread of a distribution. They compare distributions with the same mean but different MADs, and recognize that the centers are the same but the distribution with the larger MAD has greater variability or spread.

Learning Goals

Teacher Facing

  • Calculate the mean absolute deviation (MAD) for a data set and interpret what it tells us about the situation.
  • Compare (orally and in writing) the means and mean absolute deviations of different distributions, specifically those with the same MAD but different means.
  • Comprehend that “mean absolute deviation (MAD)” is a measure of variability, i.e., a single number summarizing how spread out the data set is.

Student Facing

Let's use mean and MAD to describe and compare distributions.

Learning Targets

Student Facing

  • I can use means and MADs to compare groups.
  • I know what the mean absolute deviation (MAD) measures and what information it provides.

CCSS Standards

Glossary Entries

  • mean absolute deviation (MAD)

    The mean absolute deviation is one way to measure how spread out a data set is. Sometimes we call this the MAD. For example, for the data set 7, 9, 12, 13, 14, the MAD is 2.4. This tells us that these travel times are typically 2.4 minutes away from the mean, which is 11.

    data set for travel time in minutes with a mean of 11 minutes

    To find the MAD, add up the distance between each data point and the mean. Then, divide by how many numbers there are.

    \(4+2+1+2+3=12\) and \(12 \div 5 = 2.4\)

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