To understand the relationship between two variables, we use correlation. But how we analyze that relationship depends on the type of data we’re working with — categorical or quantitative. 📚...

Sometimes the best way to learn is to walk through a full example — one that applies everything we’ve learned so far. 🎓 Real-Life Scenario Imagine you’re a school data analyst. A math teacher g...

What if you had one number and wanted to know whether it’s common or exceptional? For example, is a score of 90 on a test considered average — or much higher than typical? That’s where the Z-score...

Knowing the average (mean) of a dataset is helpful — but it’s not the whole story. Two datasets might have the same mean, yet behave very differently. That’s where measuring variability becomes ess...

Now that we’ve learned how to measure the center of data, let’s talk about how data spreads out — that’s called dispersion. 📚 This post is part of the "Intro to Statistics" series 🔙 Previou...

Before we jump into how data spreads, let’s understand how to find the center of it. 📚 This post is part of the "Intro to Statistics" series 🔙 Previously: How to Build Frequency Tables in P...

Before you build a machine learning model or even run a single analysis, there’s one thing you must do first: 📚 This post is part of the "Intro to Statistics" series 🔙 Previously: Descriptive...

When you collect data, your next big step is to understand it — and the best way to do that is to visualize it. But not every graph works for every type of data. In this post, we’ll explore: H...

Before you can do any kind of data analysis — even before you calculate a single average — you need to understand what kind of data you’re working with. Let’s start from the very beginning. ...

Before we teach a computer how to “learn,” we first need to understand our data. That’s where statistics comes in — not the scary kind with symbols and proofs, but the practical kind that helps us:...