Confused with these 2 questions as different tutors have provided 2 answers each
ID: 3131777 • Letter: C
Question
Confused with these 2 questions as different tutors have provided 2 answers each.
1. When is it OK to use a confidence interval instead of computing a p value in a hypothesis test?
Select one:
a. In any significance test.
b. In any hypothesis test with a two-sided alternative hypothesis.
c. Only when the hypothesized value of the parameter is not in the confidence interval.
d. Only when you are conducting a hypothesis test with a one-sided alternative.
e. Only when doing a test for a single population mean.
Confused between 'a' and 'b'? Can you please provide explanation.
2. Which of the following conditions doesn't need to be met before you can use a two-sample procedure?
Select one:
a. The responses in each group are independent of each other.
b. Each group is considered to be a sample from a distinct population.
c. The same variable is measured in both samples.
d. The goal is to compare the means of the two groups.
e. Data in two samples are matched together in pairs that are compared
Confused between d and e. Please explain.
Explanation / Answer
1 . A
You can use a confidence interval (CI) for hypothesis testing. In the typical case, if the CI for an effect does not span 0 then you can reject the null hypothesis. But a CI can be used for more, whereas reporting whether it has been passed is the limit of the usefulness of a test.
The reason you're recommended to use CI instead of just a t-test, for example, is because then you can do more than just test hypotheses. You can make a statement about the range of effects you believe to be likely (the ones in the CI). You can't do that with just a t-test. You can also use it to make statements about the null, which you can't do with a t-test. If the t-test doesn't reject the null then you just say that you can't reject the null, which isn't saying much. But if you have a narrow confidence interval around the null then you can suggest that the null, or a value close to it, is likely the true value and suggest the effect of the treatment, or independent variable, is too small to be meaningful (or that your experiment doesn't have enough power and precision to detect an effect important to you because the CI includes both that effect and 0).
2 D
we use two sample test not only to compare the means but also for standard deviation also like two sample F-test where we compare the variance or SD of the two samples