To rule out that a relationship between two events has been distorted by other, external factors, it is necessary to control for confounding. Confounding factors may actually be the reason we see particular outcomes, which may have nothing to do with what is being measured.

To rule out confounding, additional information must be gathered and analyzed. This includes any information that can possibly influence outcomes.

Example:

When mounting a campaign against alcohol-impaired driving, it is important to know whether other interventions aimed at road traffic safety are being undertaken at the same time. Similarly, if the campaign coincides with tighter regulations around BAC limits and with increased enforcement and roadside testing by police, it would be difficult to say whether any drop in the rate of drunk-driving crashes was attributable to the campaign or to these other measures.