What Is Statistical Mean?
If you've ever taken a math class or looked at a cereal box's back, you've probably heard about the "mean." The mean is a statistical average employed in all fields, including computer science and machine learning. If you're unsure what a means is, don't worry—you're not alone. The mean is a valuable statistical measurement for calculating an average, but it can be challenging to understand. Think of the mean as a data set's "expected" value. Using the mean with data sets with a normal distribution (a bell curve) is best. There are many types of mean, including arithmetic, mean and geometric. The calculation is the most common type of mean, calculated by adding up all the numbers in your data points. With ten students, and want to know how often each student has been late for class. You could add up all of the tardies for each student and divide by ten—the result would be the arithmetic mean. The arithmetic mean is sometimes called the "average," but that term can also refer to other means, like the geometric or harmonic mean. The arithmetic mean can be considered a data set's "average" value. The geometric mean differs slightly from the arithmetic means and helps calculate the mean of exponential values (exponentially increasing or decreasing). It might seem like an obvious way to calculate an average, but there are several ways to do it! For example, there's something called an arithmetic mean (which we described above), but there's another kind called a geometric mean. We're taking every number in our set and multiplying them together instead of adding them. Then we divide that result by the number of elements in our collection—this way, we'll get something closer to zero than if we'd used arithmetic mean!
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