Confounding Variable vs Explanatory Variable
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Confounding variable
Collecting data and study design
A confounding variable is one whose effect on the response is tangled with the explanatory variable's, so you cannot separate their two influences.
A confounding variable changes along with the explanatory variable, so its effect and the explanatory variable's effect on the response cannot be told apart. This is why an observed association does not by itself prove causation. For example, if students who attend tutoring also study more, and tutored students score higher, study time confounds the effect of tutoring. Random assignment defends against confounding by spreading such variables evenly across the treatment groups on average.
Explanatory variable
Variables and data types
An explanatory variable is the input thought to explain or predict changes in the response variable; it goes on the x-axis of a scatterplot.
The explanatory variable, also called the independent or predictor variable, is the one you treat as the cause or predictor in a relationship. It sits on the horizontal axis and supplies the values in a regression. For example, when studying how fertilizer amount affects plant height, fertilizer is the explanatory variable and height is the response. Labeling a variable explanatory reflects the question you are asking, and in an observational study it does not by itself prove that variable causes the response.