Question

A) If a test is robust, what does this allow us to do? Be as detailed as possible

B) When we state that there is homogeneity of variance, precisely which variances are homogeneous? Use words, not symbols.

C) It is important to keep the probability of making Type I equal to α. With a t-test, how do we keep the probability of Type I error in check?

Answer #1

A)

In robustness studies for hypotheses testing is very useful the p-value,observed significance level, so if a test is robust, it allows us to assess the strength of evidence against a particular null hypothesis (H0)...besides this for robustness the outcome is significant or not is only meaningful if the assumptions of the test are met or not...

Model is considered to be **robust** if its output
and forecasts are consistently accurate even if one or more of the
input variables or assumptions are drastically changed due to
unforeseen circumstances....

Note-if there is any understanding problem regarding this please feel free to ask via comment box..thank you

1) If a test is robust, what does this allow us to do? Be as
detailed as possible (although we are not looking for a long
answer).
2) When we state that there is homogeneity of variance,
precisely which variances are homogeneous? Use words, not
symbols.

1. If a test is robust, what does this allow us to do?
2.When we state that there is homogeneity of variance, precisely
which variances are homogeneous?
3.If you underestimate sigma, the standard deviation of the
population, the result is
a. the t-statstic being too small
b. a smaller probability of type i error
c. the estimated standard error being too large
d. all of the above
e. none of the above

If a test is robust, what does this allow us to do? Be as
detailed as possible (although we are not looking for a long
answer).

It is important to keep the probability of making Type I equal
to α. With a t-test, how do we keep the probability of Type I error
in check?

It is important to keep the probability of making Type I equal
to α. With a t-test, how do we keep the probability of Type I error
in check?

It is important to keep the probability of making Type I equal
to α. With a t-test, how do we keep the probability of Type I error
in check?

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The mean score on a particular math exam is 504. A test
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