Datastory Org

Interactive tools

Datastory Academy 🇸🇪


Confounding variable

A confounding variable is an outside influence that changes the effect of the cause-effect you are studying. Confounding variables can ruin an experiment and produce useless results, so it is generally important to identify potential confounding (also known as "lurking") variables to produce robust results in a scientific study.

Example of use

An example is the correlation between murder rate and the sale of ice-cream. As the murder rate rises so does the sale of ice-cream. One suggestion for this could be that murderers cause people to buy ice-cream. This is highly unlikely. A second suggestion is that purchasing ice-cream causes people to commit murder, also highly unlikely. Then there is a third variable which includes a confounding variable. It is distinctly possible that the weather causes the correlation. While the weather is icy cold, fewer people are out interacting with others and less likely to purchase ice-cream. Conversely, when it is hot outside, there is more social interaction and more ice-cream being purchased.



Få reda på när vi släpper nästa projekt. Prenumerera på vårt nyhetsbrev och följ Datastory i sociala medier.

Vi använder cookies för att ge dig en bättre upplevelse av vår webbplats. Genom att surfa vidare på Datastory, godkänner du vår användning av cookies.