Describing Data: A Statology Primer

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KDnuggets’ sister site, Statology, has a wide range of available statistics-related content written by experts, content which has accumulated over a few short years. We have decided to help make our readers aware of this great resource for statistical, mathematical, data science, and programming content by organizing and sharing some of its fantastic tutorials with the KDnuggets community.

Learning statistics can be hard. It can be frustrating. And more than anything, it can be confusing. That’s why Statology is here to help.

This collection of tutorials is on the ever-important topic of describing data. Whenever attempting to make sense of our data, being able to describe it in particular ways is important. These same descriptive tools are useful for sharing summative aspects of our data with others. Mastering the following common data description methodologies are your key to being able to understand your data better, and to better be able to understand the rest of the content on Statology.

Measures of Central Tendency: Definition & Examples

 
A measure of central tendency is a single value that represents the center point of a dataset. This value can also be referred to as “the central location” of a dataset.

In statistics, there are three common measures of central tendency:

• The mean
• The median
• The mode

Each of these measures finds the central location of a dataset using different methods. Depending on the type of data you’re analyzing, one of these three measures may be better to use than the other two.

Measures of Dispersion: Definition & Examples

 
When we analyze a dataset, we often care about two things:

1. Where the “center” value is located. We often measure the “center” using the mean and median.
2. How “spread out” the values are. We measure “spread” using range, interquartile range, variance, and standard deviation.

SOCS: A Helpful Acronym for Describing Distributions

 
In statistics, we’re often interested in understanding how a dataset is distributed. In particular, there are four things that are helpful to know about a distribution:

1. Shape
Is the distribution symmetrical or skewed to one side?
Is the distribution unimodal (one peak) or bimodal (two peaks)?

2. Outliers
Are there any outliers present in the distribution?

3. Center
What is the mean, median, and mode of the distribution?

4. Spread
What is the range, interquartile range, standard deviation, and variance of the distribution?

 
For more content like this, keep checking out Statology, and subscribe to their weekly newsletter to make sure you don’t miss anything.
 
 

Matthew Mayo (@mattmayo13) holds a master’s degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.