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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

Statologyis here to help.

This first such collection is on the topic of introductory statistics. If you have a look at the following tutorials in order, you should find that by the end of them you have a solid understanding upon which to build, and to be able to understand and utilize much of the rest of the content on Statology.

**Why is Statistics Important?**

Statistics is the field that can help us understand how to use this data to do the following things:

- Gain a better understanding of the world around us.
- Make decisions using data.
- Make predictions about the future using data.

In this article we share 10 reasons for why the field of statistics is so important in modern life.

**Descriptive vs. Inferential Statistics: What’s the Difference?**

There are two main branches in the field of statistics:

- Descriptive Statistics
- Inferential Statistics

This tutorial explains the difference between the two branches and why each one is useful in certain situations.

**Population vs. Sample: What’s the Difference?**

Often in statistics we’re interested in collecting data so that we can answer some research question.

For example, we might want to answer the following questions:

- What is the median household income in Miami, Florida?
- What is the mean weight of a certain population of turtles?
- What percentage of residents in a certain county support a certain law?

In each scenario, we are interested in answering some question about a population, which represents every possible individual element that we’re interested in measuring.

**Statistic vs. Parameter: What’s the Difference?**

There are two important terms in the field of inferential statistics that you should know the difference between: statistic and parameter.

This article provides the definition for each term along with a real-world example and several practice problems to help you better understand the difference between the two terms.

**Qualitative vs. Quantitative Variables: What’s the Difference?**

In statistics, there are two types of variables:

- Quantitative Variables: Sometimes referred to as “numeric” variables, these are variables that represent a measurable quantity.
- Qualitative Variables: Sometimes referred to as “categorical” variables, these are variables that take on names or labels and can fit into categories.

Every single variable you will ever encounter in statistics can be classified as either quantitative or qualitative.

**Levels of Measurement: Nominal, Ordinal, Interval and Ratio**

In statistics, we use data to answer interesting questions. But not all data is created equal. There are actually four different data measurement scales that are used to categorize different types of data:

- Nominal
- Ordinal
- Interval
- Ratio

In this post, we define each measurement scale and provide examples of variables that can be used with each scale.

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.