Summarizing quantitative data

This course is a part of AP®︎ Statistics, a 17-course Topic series from Khan Academy.

This unit covers common measures of center like mean and median. We'll also learn to measure spread or variability with standard deviation and interquartile range, and use these ideas to determine what data can be considered an outlier.

This course contains 5 segments:

Measuring center in quantitative data

We use measures of center like mean and median to summarize an entire set of data with just one number. Let's look at how to calculate mean and median given raw data or a data display.

More on mean and median

We look at shape and outliers to determine which measure of center is preferred for a given set of data. Let's look at why sometimes it makes sense to use mean, while other times it makes sense to use median.

Measuring spread in quantitative data

We use measures of spread to get an idea of how much variability there is in a set of data. Let's look at how to calculate and understand a few different measures of spread like interquartile range (also called IQR), variance, and standard deviation.

More on standard deviation (optional)

This collection of videos and simulations is useful if you want to develop a very deep understanding of population vs. sample standard deviation. We'll explore why we divide by n-1 when working with sample data, and look at what it means to be a biased vs. an unbiased estimator.

Box and whisker plots

Box and whisker plots show us the shape, center, spread, and even more features about a set of data. We'll learn how to construct box and whisker plots (often called box plots), and we'll also look at how to read and interpret box plots.