In hypothesis testing, we deal with Type II error by: Choosing a level of signif
ID: 3316190 • Letter: I
Question
In hypothesis testing, we deal with Type II error by: Choosing a level of significance that is acceptably low. Acting carefully and diligently in our choices of hypothesis statements and in deciding what statistical model we apply to a situation. Hedging our bets' by saying there's no evidence to reject the null hypothesis" when the p-value is larger than alpha 'Hedging our bets' by saying "there's no evidence to reject the null hypothesis" when the p-value is less than alpha Choosing a level of significance that allows us to reject the null hypothesis, so that we will always end up rejecting the null hypothesis thereby avoiding this type of error altogether. QUESTION 2 In hypothesis testing, we deal with Type III error by: Acting carefully and diligently in our choices of hypothesis statements and in deciding what statistical model we apply to a situation. Hedging our bets' by saying "there's no evidence to reject the null hypothesis" when the p-value is less than alpha. Choosing a level of significance that allows us to reject the null hypothesis, so that we will always end up rejecting the null hypothesis - thereby avoiding this type of error altogether. Hedging our bets' by saying "there's no evidence to reject the null hypothesis" when the p-value is larger than alpha Choosing a level of significance that is acceptably lowExplanation / Answer
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1)
Type I and type II errors. ... In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (also known as a "false positive" finding), while a type II error is incorrectly retaining a false null hypothesis (also known as a "false negative" finding).
B option...Acting carefully....