Central Tendency

lessons (2)

Measures of central tendency—mean, median, and mode—help us summarize data sets with single representative values. These statistics appear everywhere: average grades, median home prices, and most common shoe sizes. Understanding when to use each measure is essential for data literacy.

In this topic, you will calculate mean (average), median (middle value), and mode (most frequent). You will learn how outliers affect the mean but not the median, and why different situations call for different measures. Real data sets bring these concepts to life.

Our lessons emphasize interpretation alongside calculation. You will analyze data sets to choose appropriate measures, understand the story data tells, and recognize when statistics might be misleading. These skills are fundamental for informed decision-making in our data-driven world.

What Students Will Learn

Calculate mean, median, and mode for data sets
Understand how outliers affect each measure
Choose the appropriate measure for different contexts
Calculate weighted averages
Find the median from frequency tables
Interpret measures of central tendency in context
Recognize misleading uses of averages

Frequently Asked Questions

When should I use median instead of mean?

Use median when data has outliers or is skewed. For example, median income is more representative than mean income because a few very wealthy people can dramatically raise the mean without reflecting typical earners.

Can a data set have no mode?

Yes, if no value repeats, there is no mode. A data set can also have multiple modes if several values tie for most frequent. This is called bimodal (two modes) or multimodal.

What is a weighted average?

A weighted average counts some values more than others. For example, if a final exam is worth 40% and homework 60%, you multiply each score by its weight before averaging.