Once we have our sample, it's time to quantify our results. Suppose we wish to generalize the happiness of our employees or we want to figure out whether salaries in the company are very different from person to person.
These are some common ways of measuring our results.
Measures of center are how we define the middle, or center, of a dataset. We do this because sometimes we wish to make generalizations about data values. For example, perhaps we're curious about what the average rainfall in Seattle is or what the median height for European males is. It's a way to generalize a large set of data so that it's easier to convey to someone.
A measure of center is a value in the "middle" of a dataset.
However, this can mean different things to different people. Who's to say where the middle of a dataset is? There are so many different ways of defining the center of data. Let's take a look at a few.