Accepting null/alternate hypnothesis at various significance levels

Your Name]In summary, based on a 95% confidence interval for population proportion p of (0.11, 0.17), the correct statement regarding the hypotheses (null hypothesis) H0: p = 0.15 and (alternate hypothesis) H1: p ≠ 0.15 is c) Accept H0 at α = 0.05. This is because the confidence interval falls within the range of the null hypothesis, meaning there is no significant difference between the observed proportion and the expected proportion of 0.15. Additionally, since the confidence interval does not include the null hypothesis value, we cannot reject the null hypothesis at the 0.05 significance level.
  • #1
KLan
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Homework Statement


Given that a 95% confidence interval (or 0.05 significance level, α=0.05) for population proportion p is (0.11, 0.17), which statement is true regarding the hypotheses:
(null hypothesis) H0: p = 0.15
(alternate hypothesis) H1: p ≠ 0.15

a) Reject H0 at α = 0.05
b) Reject H0 at α = 0.10
c) Accept H0 at α = 0.05
d) Accept H0 at α = 0.10

Homework Equations





The Attempt at a Solution


Since it's a two-sided test, each side will have α = 0.025 at the 95% significance level. But p = 0.15 is within both α = 0.05 and α = 0.10 isn't it? So wouldn't H0 be accepted at both 0.05 and 0.10?
Kind of lost here. =/
 
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  • #2


Based on the information provided, the correct answer would be c) Accept H0 at α = 0.05. This is because the 95% confidence interval for the population proportion falls within the range of the null hypothesis (p = 0.15). This means that there is no significant difference between the observed proportion and the expected proportion of 0.15. Additionally, since the confidence interval does not include the null hypothesis value, we cannot reject the null hypothesis at the 0.05 significance level.

It is important to note that the significance level, α, is the probability of rejecting the null hypothesis when it is actually true. In this case, α = 0.05 means that there is a 5% chance of rejecting the null hypothesis when it is actually true. Therefore, if the observed proportion falls within the confidence interval, we cannot reject the null hypothesis at this significance level.

I hope this helps clarify your understanding. If you have any further questions, please feel free to ask. Keep up the good work in your studies!
 

Related to Accepting null/alternate hypnothesis at various significance levels

1. What is the significance level in hypothesis testing?

The significance level, also known as alpha (α), is the probability of rejecting the null hypothesis when it is actually true. It is typically set at 0.05 or 5%, meaning that there is a 5% chance of rejecting the null hypothesis even though it is true. This level is used as a threshold for determining whether the results of a study are statistically significant.

2. What is the difference between accepting and rejecting the null hypothesis?

Accepting the null hypothesis means that the results of a study do not show a significant difference between the variables being tested. This suggests that any observed differences are due to chance. On the other hand, rejecting the null hypothesis means that there is a significant difference between the variables, indicating that the results are not due to chance.

3. How do you determine the appropriate significance level for a study?

The significance level is typically set at 0.05 or 5%, but it can vary depending on the type of study and the research question. Other factors, such as the potential consequences of making a Type I or Type II error, may also influence the choice of significance level. Ultimately, the appropriate significance level should be determined by the researcher based on their knowledge of the field and the goals of the study.

4. What are Type I and Type II errors in hypothesis testing?

Type I error occurs when the null hypothesis is rejected even though it is true, while Type II error occurs when the null hypothesis is accepted even though it is false. The probability of committing a Type I error is equal to the significance level (α), while the probability of a Type II error is denoted by β. Researchers strive to minimize both types of errors in their studies.

5. Can the significance level be adjusted during the course of a study?

No, the significance level should be determined and set before the study begins. Changing the significance level after results have been collected can increase the likelihood of committing a Type I error and can undermine the validity of the study. Any adjustments to the significance level should be made with careful consideration and justification.

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