A relationship between two variables is due to an extraneous variable that has not been measured. This is known as the variable problem.

The situation you are describing is known as the confounding variable problem. In statistical analysis or research, a confounding variable is an outside influence that changes the effect of a dependent and independent variable. This extraneous variable can lead to erroneous conclusions about the relationship between the variables being studied. For example, if researchers were studying the relationship between exercise (independent variable) and weight loss (dependent variable), a confounding variable could be diet or metabolic rate, which also affects weight loss but is not considered in the analysis.

Detecting and addressing confounding variables is crucial for ensuring the validity of the results. Various statistical methods, such as multivariable regression analysis, can be utilized to control for these confounders, and randomized controlled trials can also help eliminate their influence.