Question

1. Setting the significance level cutoff at .10 instead of the more usual .05 increases the likelihood of
a. a Type I error.
b. a Type II error.
c. failing to reject the null hypothesis.
d. accepting the null hypothesis when, in fact, it is false.

2. A Type I error is the result of
a. improper measurement techniques on the part of the researcher.
b. failing to reject the null hypothesis when, in fact, it is true.
c. incorrectly rejecting the null hypothesis.
d. incorrectly accepting the null hypothesis.

3. Setting the significance level at a very extreme cutoff (such as .001) increases the chances of
a. getting a significant result.
b. rejecting the null hypothesis.
c. a Type I error.
d. a Type II error.

4. Failing to reject the null hypothesis when the research hypothesis is true is referred to as
a. the probability of rejection.
b. the error term.
c. a Type I error.
d. a Type II error.

5. Type II errors concern scientists because
a. it could mean that a good theory or beneficial practice is not used.
b. it means that the experiment must be repeated to confirm the positive result.
c. rejecting the null hypothesis should only occur when the research hypothesis is true.
d. future researchers might build entire theories based on a mistakenly significant result.

need asap please

Answer 1

Solution:

1. Setting the significance level cutoff at .10 instead of the more usual .05 increases the likelihood of

Ans :  a Type I error.

Option : a

2. A Type I error is the result of

Ans : incorrectly rejecting the null hypothesis

Option : c

3. Setting the significance level at a very extreme cutoff (such as .001) increases the chances of

Ans :  a Type II error.

Option : d

4. Failing to reject the null hypothesis when the research hypothesis is true is referred to as

Ans : a Type I error

Option : c

5. Type II errors concern scientists because

Ans : it could mean that a good theory or beneficial practice is not used.

Option : a