Hi Natalia,

I am confused. What needs clarified?

I will give you the instructor's answer, if that helps.

Instructor's example: "I want to know if your mastery excercise averages are significantly different between two different classes. In this case, I could use a two-sample hypothesis test.

Ho: pi1 = pi2

Ha: pi1 <> pi2

I could set an a priori alpha level of .05 and evaluate the p-value associated with

the Z-statistic. If the p-value is less than .05, I would reject the null. If not,

I would fail to reject the null.

Thus, a type 1 error would mean that I reject the null hypothesis of a difference between

the two classes. Doing so might mean that I incorrectly assume performance is not equal.

A type 2 error would meant that I don't reject the null when I should.

Doing so might result in an improper assessment that performance is equal."

I am confused. What needs clarified?

I will give you the instructor's answer, if that helps.

Instructor's example: "I want to know if your mastery excercise averages are significantly different between two different classes. In this case, I could use a two-sample hypothesis test.

Ho: pi1 = pi2

Ha: pi1 <> pi2

I could set an a priori alpha level of .05 and evaluate the p-value associated with

the Z-statistic. If the p-value is less than .05, I would reject the null. If not,

I would fail to reject the null.

Thus, a type 1 error would mean that I reject the null hypothesis of a difference between

the two classes. Doing so might mean that I incorrectly assume performance is not equal.

A type 2 error would meant that I don't reject the null when I should.

Doing so might result in an improper assessment that performance is equal."

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