A double-blind experiment is conducted to investigate the side effects of hormone replacement therapy for women with menopausal symptoms. The experiment randomly assigns more than 16,000 American women to either a hormone treatment or a placebo. After five years, the HRT study finds no significant difference in the proportion of women developing breast cancer and heart disease. Researchers decide, based on this finding, to allow the study to continue. As the null hypothesis was not rejected, there is a chance that the researchers made a type II error.Given the type of error made in this situation, what could researchers do to reduce the risk of this error?
Question
A double-blind experiment is conducted to investigate the side effects of hormone replacement therapy for women with menopausal symptoms. The experiment randomly assigns more than 16,000 American women to either a hormone treatment or a placebo. After five years, the HRT study finds no significant difference in the proportion of women developing breast cancer and heart disease. Researchers decide, based on this finding, to allow the study to continue. As the null hypothesis was not rejected, there is a chance that the researchers made a type II error.Given the type of error made in this situation, what could researchers do to reduce the risk of this error?
Solution
To reduce the risk of a Type II error, researchers could take several steps:
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Increase the sample size: A larger sample size can increase the power of the test, which in turn reduces the probability of a Type II error. In this case, researchers could include more women in the study.
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Increase the duration of the study: Extending the duration of the study could potentially reveal more significant differences between the hormone treatment and placebo groups. This could also reduce the chance of a Type II error.
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Use a more sensitive measurement tool: If the tools used to measure the development of breast cancer and heart disease are not sensitive enough, they might not detect small but significant differences. Using more sensitive tools could help reduce the risk of a Type II error.
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Adjust the significance level: The researchers could also consider adjusting the significance level. However, this could increase the risk of a Type I error (rejecting a true null hypothesis). Therefore, this step should be taken with caution.
Remember, a Type II error occurs when the null hypothesis is not rejected when it is false. So, these steps are all aimed at making it more likely to reject the null hypothesis when it is indeed false.
Similar Questions
Suppose that at the end of the five-year study described above, a greater proportion of the hormone-treated group have breast cancer and heart disease. This observed difference is statistically significant. Researchers are so alarmed by the results that the experiment is ended early to prevent further harm to the health of the women participating in the hormone group. Since the null hypothesis was rejected, it is possible researchers made a type I error.Given the type of error made in this situation, what could researchers do to reduce the risk of this error?
A researcher looks at the mean salaries of male and female electricians and decides based on the evidence that there is no difference between the two groups. Given that there was a failure to reject the null hypothesis, it is possible that a type II error was made.Given the type of error made in this situation, what could researchers do to reduce the risk of this error?Increase the sample size.Choose a .01 significance level, instead of a .05 significance level.
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