Undersampling

Okay kiddo, have you ever noticed that in some pictures, the colors or details look a little bit weird or blurry? That's kind of like what happens with undersampling.

When we take a picture, we use something called a camera. The camera has a little piece inside called a sensor that "sees" the picture and captures it. But sometimes, the camera doesn't capture enough of the picture to make it look good.

Undersampling is kind of like taking a teeny tiny picture of a big thing, and then trying to blow it up to make it look bigger. When we do that, the picture gets blurry and you can't see all the details very well. That's because there's not enough information in the picture to make it look good when it's blown up.

In the same way, when we undersample in science or data analysis, we're not capturing enough information about something. That means when we try to look at the whole thing, we might not see all the details or important parts.

To avoid this, scientists and data analysts use something called sampling strategies to make sure they get enough information to see the whole picture. It's kind of like taking a bigger picture instead of a teeny tiny one. That way, they can see all the important parts and make better decisions.