Chapter 3. Statistics Introduction – Artificial Intelligence for Everyone 🤖🧑

AI is changing the world, but understanding it shouldn’t be reserved for tech experts. That’s why I am currently writing Artificial Intelligence for Everyone—a book designed to make AI accessible, clear, and practical for anyone curious about this transformative technology.

Every week, I’ll be releasing a new chapter exclusively for paid subscribers on The Data Journey. You’ll get early access to insights on how AI works, its impact on society, and how to navigate the AI-driven future.

Stay ahead of the curve, and get Artificial Intelligence for Everyone delivered straight to your inbox.

👉 This week we’ll start with the introduction on Machine Learning! If you have any feedback on the chapter, feel free to write me to ivopbernardo@gmail.com

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3. Statistics Overview

Statistics on chapter 3 in a non-technical book? Am I crazy? Wait—hear me out!

Including a chapter on statistics in a non-technical book about artificial intelligence (AI) might seem unusual, but there’s a good reason for it. Statistics form the foundation of AI. At its core, AI relies heavily on data, and understanding statistical principles is essential for grasping how AI works. While many introductory AI books avoid diving into statistics early on, I believe this is the right approach to truly understand the subject. Statistical concepts enhance AI’s ability to:

· Make accurate predictions.

· Learn from data.

· Make informed decisions.

Without a solid understanding of statistics, AI’s reliability, efficiency, and progress would be severely constrained.

While the connection between statistics and AI might not always seem obvious at first, most AI algorithms are deeply rooted in statistical principles. Don’t worry—this chapter won’t overwhelm you with technical jargon or advanced mathematics. Instead, we’ll focus on the basics, providing you with the foundation needed to understand how AI processes information in a way that mirrors statistical analysis.

Step 1 of our journey in understanding AI starts with developing a skill called "statistical thinking". By learning how data transitions into actionable insights, you’ll gain a clearer perspective on AI's capabilities. This short chapter will give you the groundwork for that understanding, ensuring you’re well-prepared for the more advanced topics ahead.

Figure – Foundational Skills to Understand AI

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3.1. Descriptive Statistics and Data Types

3.1.1 Continuous Data

Embracing statistical thinking is the first skill I aim to teach you with this book. For most of us, "statistics" is a boogeyman from college or high school days. But, this discipline covers a diverse range of terms and methods essential for understanding data. Analysts, researchers and other professionals often rely on statistics to identify patterns within datasets, reflecting the way algorithms depend on data analysis to improve decision-making. And you also employ a lot of statistics during your day-to-day (without knowing it). Imagine you’re at a pizza party with your friends. Some eat 2 slices, some eat 4, and some eat none. If you want to figure out the average number of slices each person eats, you’re already using statistics!

In the end, statistics is nothing more than the science of common sense. But don’t worry, I won’t bore you too much with my statistical talk. I was the one that chose to go through a statistics major in college. Don’t want to force anyone to go through the same.