A decision rule is a procedure that the researcher uses to decide whether to accept or reject the null hypothesis.
Two types of errors can result from a decision rule.
Type I error. A Type I error occurs when the researcher rejects a null hypothesis when it is true. The probability of committing a Type I error is called the significance level, and is often denoted by α.
Type II error. A Type II error occurs when the researcher accepts a null hypothesis that is false. The probability of committing a Type II error is called Beta, and is often denoted by β. The probability of not committing a Type II error is called the Power of the test.
We have Binomial distribution for every coin
Null hypothesis is that coin is unbiased (head comes up at least twice). Alternative hypothesis is that coin is biased.
Level of significance is probability to reject true null hypothesis:
Power of the test is the probability to reject null hypothesis while alternative is true:
"1-\\beta={243 \\over 3125}+{810 \\over 3125}={1053 \\over 3125}=0.33696"
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