Which of the following statements is TRUE given that the p-value associated with a test is GREATER than 0.05? Select all that are correct. We should accept the Null Hypothesis. The result of the test is statistically insignificant. The result is likely to be observed when the Null hypothesis is true. There is a possibility that we have committed a Type II error. There is no evidence that the treatment had an effect. We should fail to reject the Alternative Hypothesis. Which of the following scenarios would lead to a DECREASE in the power of a test? Select all that are correct. Changing alpha from 0.01 to 0.05. Substituting a small effect for a medium effect. Replacing a two-tailed test with a one-tailed test. Decreasing the sample size. Decreasing the population standard deviation. Increasing the probability of a Type II error. Which of the following statements are TRUE with regards to test selection? Select all that are correct. Comparing a sample proportion to a population proportion: One-sample z-test. Comparing the means from two different samples: Independent t-test. Comparing a sample mean to a population mean where the population standard deviation is unknown: One-sample t-test. Comparing a sample mean to a population mean where the population standard deviation is known: One-sample z-test. Comparing the mean of a sample with the mean of their respective siblings: Paired t-test. Comparing the means of a sample before and after receiving a treatment intervention: Paired t-test.
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The p-value associated with a test is GREATER than .05. The correct statements are: - We should accept the Null Hypothesis. - There is a possibility that we have committed a Type II error. - There is no evidence that the treatment had an effect. Show moreā¦
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Which of the following statements is NOT true about hypothesis tests? The first assumption is that the null hypothesis (Ho) is true. Critical values are arbitrary, but are directly related to the level. The p-value is the probability (under the null hypothesis) of observing a test statistic as extreme or more extreme than the one related to the data. When the p-value is less than the level, we would not have enough evidence to reject the null hypothesis (Ho). When the test statistic is more extreme than the critical values, we would have enough evidence to reject the null hypothesis (Ho) in favor of the alternative hypothesis (Hj). In a two-sample hypothesis test for the difference in means of independent samples, the test statistic was calculated to be 2.11. The critical values for the test were t = 12.05. Which of the following is the appropriate decision? Reject Ho Fail to reject Ho Reject Hj Fail to reject Hj Depends on the p-value and level Use the table of summary statistics to determine the value of the test statistic in a hypothesis test for the difference in means of independent samples. The order of subtraction should be sample 1 minus sample 2. Sample 1 = 0.59 Sample 2 = 1.97 s1 = -6.83 s2 = -285 n1 = 14
Adi S.
Which of the following are true about p-values? Select all that apply: The p-value is the probability that the test statistic you obtained occurred due to chance. The p-value is the probability that the null hypothesis is true. The p-value is the probability that a test statistic at least as large as the one calculated would occur due to chance, assuming the null hypothesis was true. If the p-value is less than alpha (typically 0.05), fail to reject the null and declare the result "not statistically significant". If the p-value is less than alpha (typically 0.05), reject the null and declare the result "statistically significant". The p-value can be visualized as an area under the sampling distribution of the test statistic. The p-value can be visualized as a location on the horizontal axis of the sampling distribution of the test statistic. Which of the following are concerns that arise from "multiple testing"? Select all that apply: (For clarification, "multiple testing" refers to the practice of performing more than one hypothesis test as part of a single analysis.) The more hypothesis tests that are performed, the higher the chance of falsely rejecting at least one true null hypothesis. The more hypothesis tests that are performed, the lower the chance of correctly rejecting at least one false null hypothesis. Under multiple testing, we risk committing Type I error. Under multiple testing, we have an increased risk of committing Type I error. Under multiple testing, using a comparison-wise error rate of 0.05 will produce a family-wise error rate larger than 0.05. Under multiple testing, using a family-wise error rate of 0.05 will produce a comparison-wise error rate larger than 0.05. Suppose we fail to reject Ho / H1 = H2. Which of the following are appropriate interpretations of this statistical decision? Select all that apply: We have statistical evidence that the null hypothesis is true. We do not have statistical evidence that the null hypothesis is false. If we create a 95% confidence interval for H1 - H2, it will not contain zero. If we create a 95% confidence interval for H1 - H2, it will contain zero. If we conduct the same hypothesis test using data from a new random sample, there is at least a 95% chance that the null hypothesis will not be rejected. If we conduct the same hypothesis test using data from a new random sample, there is at most a 5% chance that the null hypothesis will not be rejected. Knowing that we fail to reject the null with this data does not tell us the probability that we would fail to reject the null with new data.
PROBLEM #1: For each of the following statements, determine whether it is true or false. Label "T" if it is true, otherwise label "F". a. The null hypothesis is the claim that is initially assumed to be true while the alternative hypothesis is the assertion that is contradictory to the null hypothesis. They are two competing hypotheses. b. Depending on the form of the alternative hypothesis, we have three different forms of hypothesis tests, namely, upper-tailed, lower-tailed, and two-tailed tests. c. A test statistic and a rejection region are two basic ingredients of a hypothesis test. d. For a hypothesis test, we have two results: rejecting H0 and failing to reject H0. e. P-value is the probability of obtaining results as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. f. For a hypothesis test, if the calculated P-value is less than or equal to the specified significance level, we should reject the null hypothesis. g. In a hypothesis test, Type I error is the error made when the null hypothesis is rejected when in fact the null hypothesis is true; Type II error is the error made when the null hypothesis is not rejected when it is false. h. For a hypothesis test regarding the population mean, we should use t-test if the underlying distribution of the observations is normal, the sample size is not large enough, and the population variance is unknown. However, if we know the population variance, we should use z-test. i. For a z-test regarding the population mean, if Ha : Ī¼ > Ī¼0 is the alternative hypothesis, then the rejection region should be z ā„ zĪ± where zĪ± is the z critical value associated with the significance level Ī±. j. For a t-test regarding the population mean, if Ha : Ī¼ > Ī¼0 is the alternative hypothesis, then the rejection region should be t > tĪ±,nā1 where tĪ±,nā1 is the t critical value associated with the significance level Ī± (suppose that the given sample is of size n).
Madhur L.
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