User talk:Ottesen07

Type I and Type II errors in statistics

A Type I error is when one wrongfully rejects the null hypothesis in a statistical study. A Type II error is when one wrongfully accepts the null hypothesis in a statistical study. Typically, the null hypothesis is the hypothesis that opposes what is being studied. For example, if an automotive company is studying the effect a fuel additive will have a positive effect on a car's fuel economy, the null hypothesis might be, "the fuel additive has no effect on the test vehicle's fuel economy." If the fuel additive indeed has no effect, and those conducting the study reject the null hypothesis, they have committed a Type I error. If the fuel additive does have a positive effect, but those conducting the study accept the null hypothesis, they have committed a Type II error.

See http://en.wikipedia.org/wiki/Type_I_and_type_II_errors