Question

1) A false positive rate is the rate of falsely rejecting the Ho i.e. null hypothesis for a particular problem.

2) The goal of a hypothesis testing is to provide an evidence over the insignificance of of the alternate hypothesis i.e. the possibilty of not rejecting the null hypothesis.

3) A Type I error is defined as the possibility of rejecting Ho when Ho is true in nature i.e.
P(Type I error) = P(Rejecting Ho / Ho is true) <=
i.e. the alpha level is the maximum value of P(Type I error).

4) When the results of a hypothesis test are statistically significant, then we conclude to reject the null hypothesis i.e. we accept the claim given in the alternative hypothesis.

5) In a hypothesis testing, a p-value is the probability of obtaining any possible extremes along with the assumption of the null hypothesis being true in nature. A p-value is used in making a decision about the rejection/ acceptance of a null hypothesis i.e. when the p-value < , we reject the null hypothesis.

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