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Sister site of KDnuggets, Statisticshas a wide range of statistics-related content available, written by experts, content that has been accumulating over a few miniature years. We decided to aid our readers become aware of this great source of statistics, math, 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 most of all, it can be confusing. That’s why Statistics is here to aid.
This first such collection covers the topic of introductory statistics. If you work through the following tutorials in order, you should find that by the end you will have a solid understanding to build on, and you will be able to understand and operate most of the rest of the content in Statology.
Why are statistics important?
Statistics is a field that can aid us understand how to operate data to do the following:
- Gain a better understanding of the world around us.
- Make decisions with data.
- Make data-driven predictions about the future.
In this article, we share 10 reasons why statistics are so critical in current life.
Descriptive vs. Inferential Statistics: What’s the Difference?
In the field of statistics, there are two main branches:
- Descriptive Statistics
- Inference Statistics
This tutorial explains the differences between the two branches and why each is useful in certain situations.
Population vs. Sample: What’s the Difference?
Often in statistics we are interested in collecting data so that we can answer some research questions.
For example, we might want to answer the following questions:
- What is the median household income in Miami, Florida?
- What is the average weight of a specific population of turtles?
- What percentage of a county’s residents support a particular law?
In each scenario, we want to answer some question about the population that represents every possible individual item we want to measure.
Statistics vs. Parameter: What’s the Difference?
There are two critical terms in the field of inferential statistics that you should know the difference between: statistic and parameter.
In this article, you will find a definition of each term, a real-life example, and some practice tasks to aid you better understand the difference between the two terms.
Qualitative and Quantitative Variables: What’s the Difference?
In statistics, there are two types of variables:
- Quantitative Variables: Sometimes called “numeric” variables, these are variables that represent a measurable quantity.
- 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.
Levels of measurement: nominal, ordinal, interval and ratio
In statistics, we operate data to answer engaging questions. But not all data is created equal. There are actually four different scales of data measurement that are used to categorize different types of data:
- Nominal
- Ordinal
- Interval
- Ratio
In this post, we will define each measurement scale and provide examples of variables that can be used in each scale.
For more content like this, keep an eye on the Statology website and sign up for their weekly newsletter to make sure you don’t miss a thing.
Matthew Mayo (@mattmayo13) holds a Master’s degree in Computer Science and a postgraduate diploma in Data Mining. As Managing Editor of KDnuggets & Statisticsand contributing editor at Mastery in Machine LearningMatthew aims to make sophisticated data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploration of emerging artificial intelligence. He is driven by a mission to democratize knowledge within the data science community. Matthew has been coding since he was 6 years senior.