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type I error
occurs if a test rejects the null when the null is true. that is, the test finds convincing evidence that the alternative hypothesis is true when it really isn’t
type II error
occurs if a test fails to reject the null when the null is true. that is, the test does not find convincing evidence that the alternative hypothesis is true when it is
as sample size increases
test power: increases
type I: stays the same
type II: decreases
probability of a type I error
the significance level is the probability of a type I error
probability of a type II error
a type II error is inversely related to the probability of a type I error
conclusion
because our p-value is 0.637 > a = 0.05, we fail to reject the null. there is not convincing evidence that the true proportion of [context] differs from 25%
when the p-value is less than the significance value
we reject the null
when the p-value is more than the significance value
we fail to reject the null
as significance level increases
test power: increases
type I: increases
type II: decreases
as the null and true value get farther apart
test power: increases
type I: stays the same
type II: decreases
Note 
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