Type I and Type II Errors

This goes along with hypothesis testing.

If you've rejected the null, found reason to believe that your research hypothesis might have some truth to it, but in reality you've made a mistake, then you made a Type I error.

If our test statistic falls into the rejection region (p < or = .05) then we are saying that 19 times out of 20 we're correct in our decision to reject the null. But we're going to wrong 1 time in 20, 5 percent of the time.

To make sure we don't do that, we can decrease the rejection region, make it .01, only 1 chance in a hundred that we reject the null by mistake.

We call the probability of making a Type I error alpha. The probability of making a Type II error is beta.

A Type II error occurs when we accept a false null hypothesis.

The ability of a statistical test to reject the null hypothesis when it is false and the research hypothesis is true is called the POWER of the test. The best way to increase this power is to increase the size of the sample.