Accuracy vs. Precision: What’s the Difference in Data?

When you're starting out in data science, engineering, or analytics, it's easy to hear terms like accuracy and precision tossed around — especially when working with models or measurements. But what do they actually mean?

Let’s break it down 👇

Accuracy

Accuracy refers to how close your measurement is to the actual or true value. Think of it as hitting the bullseye on a dartboard. If your data is accurate, it’s on target.

🧠 Example:
If a weather sensor reports 25°C when the actual temperature is 25.2°C, it's pretty accurate.

Precision

Precision, on the other hand, is about how consistent your results are — even if they’re not right. Imagine throwing darts that all hit the same spot, but far from the bullseye. That’s precision without accuracy.

🧠 Example:
If the same weather sensor always reports 23.0°C, even if the real temp varies, it's precise but not accurate.

Why It Matters in Data Work

In data modeling or machine learning, you’ll often trade off accuracy and precision, depending on the problem you're solving. For example:

• In classification tasks, you might track precision to see how often your positive predictions are correct.

• In measurements, you care about accuracy when making real-world decisions.

Here is detail explaination to further understand the topic and how it relates to model recall from evidently.ai .