Introduction to Statistics: An Introduction to Statistics

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 first collection of its kind is all about the topic of introductory statistics. If you take a look at the following tutorials in order, you should find that by the end of them you will have a solid understanding to build on and will be able to understand and use much of the rest of the content in 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.
• Making predictions about the future using data.

In this article we share 10 reasons 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 is useful in certain situations.

Population versus sample: what's the difference?

In statistics, we are often interested in collecting data in order to answer a research question.

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

1. What is the average household income in Miami, Florida?
2. What is the average weight of a given population of turtles?
3. What percentage of residents in a given county support a given law?

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

Statistics vs. Parameter: What's the Difference?

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

This article provides the definition of 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:

1. Quantitative variables: Sometimes called “numeric” variables, are variables that represent a measurable quantity.
2. Qualitative variables: Sometimes called “categorical” variables, these are variables that take names or labels and can fit into categories.

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

Measurement levels: 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:

1. Nominal
2. Ordinal
3. Interval
4. Relationship

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

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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.