When testing the following hypotheses at an a level of significance Ho: p 0.7, Ha: p > 0.7 The null hypothesis will be rejected if the test statistic z is a. z > za b. z < za c. z < -za d. none of the above If a p-value for a 2-sided test equals .065, the p-value for the 1-sided test using the same sample data will not be significant at the 1% level. True False The alternative hypothesis states that there is no difference/no effect. True False Which of the following does not need to be known in order to compute the P-value? knowledge of whether the test is one-tailed or two-tailed the value of the test statistic the level of significance all of the above are needed none of the above are needed If our sample value is close to the null/population value, we conclude that we can neither accept or reject the null. something happened in the study; there is a significant effect. nothing happened in the study; there is no effect. something happened in the study, but the effect is very small. When a p-value is high, this means there is strong evidence against the null hypothesis True False Let's suppose we conduct a hypothesis test about the mean value of something, and determine that we should reject the null hypothesis. What does that mean? Our hypothesized mean has been proven incorrect. The difference between our sample mean and our hypothesized mean was most likely due to random chance. The difference between our sample mean and our hypothesized mean was statistically significant. The difference between our sample mean and our hypothesized mean was not statistically significant. Let's suppose we conduct a hypothesis test about the mean value of something, and determine that we should reject the null hypothesis. What does that mean? Our hypothesized mean has been proven incorrect. The difference between our sample mean and our hypothesized mean was most likely due to random chance. The difference between our sample mean and our hypothesized mean was statistically significant. The difference between our sample mean and our hypothesized mean was not statistically significant. Hypotheses in a significance test are always stated in terms of the population parameters. True False A statistics professor announces to the class that a majority of students who take his class earn an A. A skeptical group of students suspects that this is too good to be true. If they were going to conduct a hypothesis test, what would the appropriate hypotheses be? Ho: p < 0.50, Ha: p = 0.50 Ho: p = 0.50, Ha: p < 0.50 Ho: p > 0.50, Ha: p = 0.50 Ho: p = 0.50, Ha: p > 0.50 If our P-value is really small, that indicates which of the following? (Select all that apply) The probability of our observed results occurring given the null hypothesis is very small, thus the null hypothesis is probably not true. The null hypothesis is true. The probability of our observed results occurring given the null hypothesis is very small, thus the null hypothesis is probably true. We should fail to reject the null hypothesis. We should reject the null hypothesis. The null hypothesis is false. Many people describe hypothesis testing as counter-intuitive because we test whether something happened in order to conclude that nothing happened. we test whether something happened but can still conclude that nothing happened. we can only conclude that nothing happened when we are 100% sure that something did not happen. we test whether nothing happened in order to conclude that something happened. A t-test is used when the population standard deviation for a mean is unknown. True False When a sample statistic is close to the hypothesized population parameter, the p-value for a significance test will typically be low. True False