Question

Discuss the implications of Confounding Variables and how they effect the primary goal of statistics; that of prediction.

Answer 1

A confounding variable is an outside influence that changes the effect of a dependent and independent variable. This extraneous influence is used to influence the outcome of an experimental design. Simply, a confounding variable is an extra variable entered into the equation that was not accounted for. Confounding variables can ruin an experiment and produce useless results. They suggest that there are correlations when there really are not.

For example, Consider a study is done to reveal whether bottle-feeding is related to an increase of diarrhea in infants. It would appear logical that the bottle-fed infants are more prone to diarrhea since water and bottles could easily get contaminated, or the milk could go bad. However, the facts are that bottle-fed infants are less likely to get diarrhea than breast-fed infants. Bottle feeding actually protects against illness. The confounding variable would be the extent of the mother's education on the matter. If you take the mother's education into account, you would learn that better educated mothers are more likely to bottle-feed infants.