Data Description: An Introduction to Statology

KDnuggets' sister site, StatologyKDnuggets has a wide range of statistics-related content available written by experts, content that has been accumulated over a few years. We have decided to help our readers become familiar with this great resource of statistics, mathematics, data science, and programming content by organizing and sharing some of its fantastic tutorials with the KDnuggets community.

Learning statistics can be difficult, frustrating, and most of all, confusing. That's why Statology It's here to help.

This collection of tutorials covers the ever-important topic of data description. Whenever we try to make sense of our data, it's important to be able to describe it in specific ways. These same descriptive tools are useful for sharing summative aspects of our data with others. Mastering the following common data description methodologies is key to better understanding your data and to better understanding the rest of the content in Statology.

Measures of central tendency: definition and examples

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

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

• The meaning
• The median
• The mode

Each of these measures finds the center location of a data set using different methods. Depending on the type of data you are analyzing, one of these three measures may be better than the other two.

Measures of dispersion: definition and examples

When analyzing a data set, we often worry about two things:

1. Where is the “center” value? We often measure the “center” using the mean and median.
2. How “dispersed” the values ​​are. We measure “dispersion” using the range, interquartile range, variance, and standard deviation.

SOCS: A useful acronym for describing distributions

In statistics, we are often interested in understanding how a data set is distributed. In particular, there are four things that are useful to know about a distribution:

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

2. Outliers
Are there outliers present in the distribution?

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

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

For more content like this, keep checking back at Statology and sign up for their weekly newsletter to make sure you don't miss a thing.

Matthew May (twitter.com/mattmayo13″ rel=”noopener”>@mattmayo13) holds a master's degree in computer science and a postgraduate diploma in data mining. As editor-in-chief of KDnuggets & Statologyand contributing editor at Mastering Machine LearningMatthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging ai. His mission is to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.