uncertainty: testing errors
- inferential statistics: limited information from samples can lead to incorrect conclusions
- two kinds of errors can be made in a hypothesis test
A type I error occurs when a null hypothesis is rejected that is actually true. In a typical research situation, a Type I error means that the researcher concludes that a treatment does have an effect when in fact the treatment has no effect. (unlikely but critical)
A type II error occurs when a null hypothesis is not rejected that is really false. In a typical research situation, a Type II error means that the test has failed to detect a real treatment effect. (insufficient evidence, weak conclusion)
The alpha level for a hypothesis test is the proba- bility that the test will lead to a Type I error.