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Is it not the other way around? The p-value is 0.07 which is greater than 0.05. This would mean that the p-value is “significant”. The error type is a “false positive” since he is rejecting the null hypothesis in favor of the alternative.
No.
If the significance threshold is 0.05, then 0.07 is greater than 0.05 and incorrect.
The researcher accepts that the students who take the test in an ergonomic chair is not significantly different from the null…So, they accepted the null hypothesis, which is incorrect. In order to reject the null, the p-value has to be less than 0.05 (0.04, 0.01, 0.000009, etc).
From the hint:
“A p-value of 0.07 is greater than the significance threshold, and would lead the researcher to conclude (incorrectly) that the average score for students in an ergonomic chair is not significantly different from 50 points.” The researcher made a Type II error, or a false negative.
I think there is some confusion about what a significant finding is. If the p-value is not significant and you have met all your assumptions, then you retain the null hypothesis. if you had rejected it (when there is no difference) then that is a type 1 error. if the p-value is significant then you would reject the null hypothesis. if you accepted the alternative hypothesis when there is no significant difference (ie the null hypothesis is true) then that is a type 2 error. In the case of this question, there is no type 1 or type 2 error as the only information you are given about the test result is the p-value and based on the three results (correct, type-1, type-2), the test has been run correctly. so the correct response is correct. the question and description dont say whether or not the researcher rejected or retained the null hypothesis (only the actual stats which the researcher doesn’t know). this is a poorly designed tutorial question.
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