Absorption rates are important considerations in the creation of a generic version of a brand-name drug. A pharmaceutical company wants to test if the absorption rate of a new generic drug (G) is the same as its brand-name counterpart (B). They run a small experiment to test H subscript 0 : space mu subscript G minus mu subscript B equals 0 against the alternative H subscript A : space mu subscript G minus mu subscript B not equal to 0. Which of the following is a Type I error? a. Deciding that the absorption rates are different, when in fact they are not. b. The researcher cannot make a Type I error, since he has run an experiment. c. Deciding that the absorption rates are different, when in fact they are. d. Deciding that the absorption rates are the same, when in fact they are. e. Deciding that the absorption rates are the same, when in fact they are not.

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Mathematics

Frida Stephens

13 August, 04:14

Absorption rates are important considerations in the creation of a generic version of a brand-name drug. A pharmaceutical company wants to test if the absorption rate of a new generic drug (G) is the same as its brand-name counterpart (B). They run a small experiment to test H subscript 0 : space mu subscript G minus mu subscript B equals 0 against the alternative H subscript A : space mu subscript G minus mu subscript B not equal to 0. Which of the following is a Type I error? a. Deciding that the absorption rates are different, when in fact they are not. b. The researcher cannot make a Type I error, since he has run an experiment. c. Deciding that the absorption rates are different, when in fact they are. d. Deciding that the absorption rates are the same, when in fact they are. e. Deciding that the absorption rates are the same, when in fact they are not.

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Julianne · Answer 1

The type 1 error here is a. Deciding that the absorption rates are different, when in fact they are not.

Step-by-step explanation:

A type I error is the rejection of a true null hypothesis (also known as a "false positive" finding or conclusion).

More generally, a Type I error occurs when a significance test results in the rejection of a true null hypothesis. By one common convention, if the probability value is below 0.05, then the null hypothesis is rejected.

In inferential statistics, the null hypothesis is a general statement or default position that there is nothing significantly different happening, like there is no association among groups or variables, or that there is no relationship between two measured phenomena.