Question 1 p-values and their limitations. Consider a "right-sided" hypothesis test with Ho : 0 = 0 vs. H₁ : 0 > 0. Suppose the test statistic, W, is observed to take the value w. That is, if 0 = 0, W has a known distribution (e.g. Normal, t, chi-square, F, etc.). (a) Consider two definitions of a “p-value": (1) The probability of getting data as extreme or more extreme than what you observed, under the null hypothesis. (2) The smallest level of significance a for which the observed data indicate that the null hypothesis should be rejected. Explain how these definitions are equivalent and mean that the p-value is calculated as P(W > w|0 = 0). (Hint: it may help to draw the null distribution, and it doesn't matter too much what it looks like). To make it simpler, this is only referring to a right-tailed test/p-value. Draw a density, mark the rejection region, mark where the test statistic

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Question 1 p-values and their limitations. Consider a "right-sided" hypothesis test with Ho :
0 = 0 vs. H₁ : 0 > 0. Suppose the test statistic, W, is observed to take the value w. That is, if
0 = 00, W has a known distribution (e.g. Normal, t, chi-square, F, etc.).
(a) Consider two definitions of a “p-value": (1) The probability of getting data as extreme
or more extreme than what you observed, under the null hypothesis. (2) The smallest
level of significance a for which the observed data indicate that the null hypothesis should
be rejected. Explain how these definitions are equivalent and mean that the p-value is
calculated as P(W > w|0 = 0). (Hint: it may help to draw the null distribution, and it
doesn't matter too much what it looks like).
To ake it simpler, this is only referring to a right-tailed test/p-value.
Draw a density, mark the rejection region, mark where the test statistic
will fall, indicate the p-value. See 11/18 slides.
Transcribed Image Text:Question 1 p-values and their limitations. Consider a "right-sided" hypothesis test with Ho : 0 = 0 vs. H₁ : 0 > 0. Suppose the test statistic, W, is observed to take the value w. That is, if 0 = 00, W has a known distribution (e.g. Normal, t, chi-square, F, etc.). (a) Consider two definitions of a “p-value": (1) The probability of getting data as extreme or more extreme than what you observed, under the null hypothesis. (2) The smallest level of significance a for which the observed data indicate that the null hypothesis should be rejected. Explain how these definitions are equivalent and mean that the p-value is calculated as P(W > w|0 = 0). (Hint: it may help to draw the null distribution, and it doesn't matter too much what it looks like). To ake it simpler, this is only referring to a right-tailed test/p-value. Draw a density, mark the rejection region, mark where the test statistic will fall, indicate the p-value. See 11/18 slides.
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