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Descriptive Statistics

In this article, the value of descriptive statistics to summarise large amounts of data is discussed.
woman with graphs on laptop listening to colleague
© Bond University

Often, we don’t struggle from a lack of data but instead too much of it. By having too much information, it is difficult to identify important features of the data and then make data-driven decisions. This is where descriptive statistics come in. Descriptive statistics can be used to describe characteristics of vast quantities of data in just a few numbers, making them a useful addition to your data analysis toolbox.

So what can descriptive statistics tell us? What insights can they provide? Well, we might care about ‘typical’ customers, for which we can use measures of central location like the mean, median and mode. We might care about how much variety there is in our data, for which we can use measures of spread like variance or standard deviation. We might care about if we tend to have a few really large or really small values, for which we can use measures of shape like skewness. Finally, we might care about how related two different variables might be, for which we can use measures of association like correlation.

In this activity, you’ll learn about all of these measures. You’ll learn what they are, how they are calculated, how to interpret them, and how to generate them using Excel.

Let’s discuss

When have you used descriptive statistics or seen them used effectively?
© Bond University
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Data Analytics for Decision Making: An Introduction to Using Excel

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