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The critical value represents the probability ofobtaining our results if the null hypothesis is false.TrueFalseThere is always a possibility that the decision reach...

Question

The critical value represents the probability ofobtaining our results if the null hypothesis is false.TrueFalseThere is always a possibility that the decision reachedin a hypothesis test is incorrect.TrueFalseA statistically significant treatment effect alwaysindicates a large treatment effect.TrueFalse

The critical value represents the probability of obtaining our results if the null hypothesis is false. True False There is always a possibility that the decision reached in a hypothesis test is incorrect. True False A statistically significant treatment effect always indicates a large treatment effect. True False



Answers

True or False?Determine whether the statement is true or false. If it is false, rewrite it as a true statement. The level of significance is the maximum probability you allow for rejecting a null hypothesis when it is actually true.

In this exercise, we're going to be answering with true or false. Uh and then explaining our answer forgiven statement and the statement goes like this. If it is important not to reject a true non hypothesis, the hypothesis test should be performed at a small significance level, true or false and the answer is true. Let's look at the sentence, the statement again, he's seen. It is important not to reject a true national hypothesis. In other words, it is important not to make a type one error. Okay, Okay. If it is important not to make a type one error, should we perform the the the test at a small significance level Alpha? Yes, the alpha should be small because we want to reduce the probability Of making a Type one error. In other words, we want to reduce the type one error probability and remember the tape one probability uh Type one error probability is alpha. So if we perform the test with a small significance level, then uh it will be important. We will reduce the chances of rejecting. And now hypotheses when it is in fact true.

A large P value in a test will favor rejection off the null hypothesis. This is false. A smaller P value will favor the rejection off null hypothesis the people. You should be small if I want to reject the null hypothesis.

We want to answer true or false for this question. When testing a hypothesis via the P value approach, if the P value is large, then reject the null hypothesis H. Now to determine whether or not the statement is true or false weeds or make sure that we understand the P value approach the hypothesis testing, so remember that we reject the null hypothesis H not when P is essential to alpha. P. Here is a P value and Alpha, as I've noted, is the confidence level. So when you have a P value that's less than or equal to alpha, that is smaller than your alpha, You reject H. nine. So already we see that a large p value is problematic for this assumption, but when we factored to account The alpha is usually some value like .01.05.1. We typically don't go beyond 10 confidence. The answer becomes even clearer. That is this is false. Because if we have a P value that is large, it's probably going to be larger than alpha, since alpha is traditionally small, and therefore we wouldn't want to reject H, not when the P value is large in order to meet the conditions described here.

If you decide to reject the null hypothesis, then you support the alternative hypothesis. Is this true or false? This is a true statement. This is true.


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