AP statistics chapter 21 by Stats modelling the world third edition by David E. Bock

● Alpha level - The threshold P-value that determines when we reject a null
hypothesis. If we observe a statistic whose P-value based on the null hypothesis
is less than , we reject that null hypothesis.
● Statistically significant - When the P-value falls below the alpha level, we say that
the test is “statistically significant” at that alpha level.
● Significance level - The alpha level is also called the significance level, most often
in a phrase such as a conclusion that a particular test is “significant at the 5%
significance level.”

● Type I error - The error of rejecting a null hypothesis when in fact it is true (also
called a “false positive”). The probability of a Type I error is .
● Type II error - The error of failing to reject a null hypothesis when in fact it is false
(also called a “false negative”). The probability of a Type II error is commonly
denoted and depends on the effect size.
● Power - The probability that a hypothesis test will correctly reject a false null
hypothesis is the power of the test. To find power, we must specify a particular
alternative parameter value as the “true” value.
● Effect size - The difference between the null hypothesis value and true value of a
model parameter is called the effect size.