Step-by-step explanation:
An explanatory variable is a type of independent variable. The two terms are often used interchangeably. But there is a subtle difference between the two. When a variable is independent, it is not affected at all by any other variables. When a variable isn’t independent for certain, it’s an explanatory variable.
Let’s say you had two variables to explain weight gain: fast food and soda. Although you might think that eating fast food intake and drinking soda are independent of each other, they aren’t really. That’s because fast food places encourage you to buy a soda with your meal. And if you stop somewhere to buy a soda, there’s often a lot of fast food options like nachos or hot dogs. Although these variables aren’t completely independent of each other, they do have an effect on weight gain. They are called explanatory variables because they may offer some explanation for the weight gain.
The line between independent variables and explanatory variables is usually so unimportant that no one ever bothers. That’s unless you’re doing some advanced research involving lots of variables that can interact with each other. It can be very important in clinical research. For most cases, especially in statistics, the two terms are basically the same.
