Type I and type II errors. ... In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding or conclusion), while a type II error is the failure to reject a false null hypothesis (also known as a "false negative" finding or conclusion).
Harder
If alpha is changed from .05 to .01, it is easier or harder to make a Type I Error?
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