4.4 quiz

D

A Type I error occurs by not rejecting the null hypothesis when the null hypothesis is true. not rejecting the null hypothesis when the null hypothesis is false. rejecting the null hypothesis when the null hypothesis is false. rejecting the null hypothesis when the null hypothesis is true.

D

A Type II error occurs by rejecting the null hypothesis when the null hypothesis is true. not rejecting the null hypothesis when the null hypothesis is true. rejecting the null hypothesis when the null hypothesis is false. not rejecting the null hypothesis when the null hypothesis is false.

A

A pharmaceutical company is testing to see whether its new drug is significantly better than the existing drug on the market.It is more expensive than the existing drug. Which makes more sense to use, a relatively large significance level (such as αα=0.10) or a relativelysmall significance level (such as αα=0.01), for the company? Large Small

A

A pharmaceutical company is testing to see whether its new drug is significantly better than the existing drug on the market.It is more expensive than the existing drug. Which makes more sense to use, a relatively large significance level (such as αα=0.10) or a relativelysmall significance level (such as αα=0.01), for the consumers? Small Large

A

By replicating a study and finding significant results again, we can be more confident that theresults are indeed significant. True False

A

Classify the conclusion of the significance test as a Type I error, a Type II error, or No error. A manufacturer claims that the mean amount of juice in its 16 ounce bottles is 16.1 ounces. Aconsumer advocacy group wants to perform a significance test to determine whether the meanamount is actually less than this. The hypotheses are:H0: μ = 16.1 ouncesHa: μ < 16.1 ouncesSuppose that the results of the sample lead to rejection of the null hypothesis. Classify thatconclusion as a Type I error, a Type II error, or a correct decision, if in fact the mean amount ofjuice, μ, is less than 16.1 ounces. No error Type I error Type II error

A

For a given level of significance, increasing the sample size will ____________________ theprobability of committing a Type I error if the alternative hypothesis is true. not affect sometimes increase sometimes decrease always decrease always increas

B

For a given level of significance, increasing the sample size will ____________________ theprobability of committing a Type II error if the alternative hypothesis is true. not affect decrease sometimes decrease always increase sometimes increase

D

For the given significance test, explain the meaning of a Type I error, a Type II error, or a correct decision as specified. A health insurer has determined that the "reasonable and customary" fee for a certain medicalprocedure is $1200. They suspect that the average fee charged by one particular clinic for thisprocedure is higher than $1200. The insurer performs a significance test to determine whether theirsuspicion is correct using αα = 0.05. The hypotheses are:H0: μ = $1200Ha: μ > $1200If the P-value is 0.09 and a decision error is made, what type of error is it? Explain. Type II error. We conclude that the average fee charged for the procedure is higher than $1200 when it actually is not higher. Type I error. We conclude that the average fee charged for the procedure is not higher than $1200 when it actually is higher. Type I error. We conclude that the average fee charged for the procedure is higher than $1200 when it actually is not higher. Type II error. We conclude that the average fee charged for the procedure is not higher than $1200 when it actually is higher.

C

For the given significance test, explain the meaning of a Type I error, a Type II error, or a correct decision as specified. At one school, the average amount of time tenth-graders spend watching television each week is21.6 hours. The principal introduces a campaign to encourage the students to watch less television.One year later, the principal performs a significance test using αα = 0.05 to determine whether theaverage amount of time spent watching television per week has decreased. The hypotheses are:H0: μ = 21.6 hoursHa: μ < 21.6 hoursIf the P-value = 0.04 and a decision error is made, what type of error is it? Explain. Type II error. We conclude that the average amount of time spent watching television each week is 21.6 hours when it is in fact less. Type I error. We conclude that the average amount of time spent watching television each week is 21.6 hours when it is in fact less. Type I error. We conclude that the average amount of time spent watching television each week is less than 21.6 hours when it in fact is not. Type II error. We conclude that the average amount of time spent watching television each week is less than 21.6 hours when it in fact is not.

C

In the past, the mean lifetime for a certain type of flashlight battery has been 9.5 hours. Themanufacturer has introduced a change in the production method and wants to perform asignificance test to determine whether the mean lifetime has increased as a result. The hypotheses are:H0: μ = 9.5 hoursHa: μ > 9.5 hoursSuppose that the results of the sample lead to rejection of the null hypothesis. Classify thatconclusion as a Type I error, a Type II error, or a correct decision, if in fact the mean running timehas not increased. Type II error No error Type I error

B

In the past, the mean lifetime for a certain type of flashlight battery has been 9.6 hours. Themanufacturer has introduced a change in the production method and wants to perform asignificance test to determine whether the mean lifetime has increased as a result. The hypotheses are:H0: μ = 9.6 hoursHa: μ > 9.6 hoursSuppose that the results of the sample lead to nonrejection of the null hypothesis. Classify thatconclusion as a Type I error, a Type II error, or a correct decision, if in fact the mean running timehas increased. Type I error Type II error

B

In the situation below, indicate whether it makes more sense to use a relatively large significance level (such as αα=0.10) or arelatively small significance level (such as αα=0.01). Testing a new drug with potentially dangerous side effects to see if it is significantly better than thedrug currently in use. If it is found to be more effective, it will be prescribed to millions of people Large Small

A

In the situation below, indicate whether it makes more sense to use a relatively large significance level (such as αα=0.10) or arelatively small significance level (such as αα=0.01). Testing to see whether taking a vitamin supplement each day has significant health benefits. Thereare no (known) harmful side effects of the supplement. Large Small

D

In the situation below,describe what it means in that context to make a Type I and Type II error. Testing a new drug withpotentially dangerous side effects to see if it is significantly better than the drug currently in use. If it is found to be moreeffective, it will be prescribed to millions of people. Making a Type I error means We do not find enough evidence that the new drug is more effective but it really is more effective. none of these We find evidence that the new drug is more effective. We find evidence that the new drug is more effective but it is really not any better. We do not find any difference between the drugs.

D

In the situation below,describe what it means in that context to make a Type I and Type II error. Testing a new drug withpotentially dangerous side effects to see if it is significantly better than the drug currently in use. If it is found to be moreeffective, it will be prescribed to millions of people. Making a Type II error means: We find evidence that the new drug is more effective. none of these We find evidence that the new drug is more effective but it is really not any better. We do not find enough evidence that the new drug is more effective but it really is more effective. We do not find any difference between the drugs.

B

Smaller sample sizes make it easier to achieve statistical significance if the alternative hypothesis istrue. True False

B

Suppose 1000 tests are run to test a null hypothesis using αα =0.05. If the null hypothesis is true,about how many of these tests would you expect to show statistically significant results? 0 50 5 cannot be determined from the information given 1000

B

The level of significance, αα, is the probability of making a Type β error Type I error Type II error