Question

True or False

1) If the obtained sample data are in the critical region, then we could conclude that the data provide support for the null hypothesis.

2) If a researcher is predicting that a treatment will increase scores, then the critical region for a directional test will be in the right-hand tail

3) Whenever the statistical decision is to reject the null hypothesis, there is risk of a type I error

4)In a type II error, the experimenter concludes there is evidence for an effect when in fact an effect does not exist

5) All other things being equal (Mean, Standard Deviation, M and a), You are more likely to make a Type I error with a sample of n=4 than with a sample of n=11

6) If the null-hypothesis is rejected using a one-tailed test, then it certainly would be rejected if the researcher used a two-tailed test.

7) Increasing the alpha level (for example from .01 to .05) will decrease the size of the critical region.

8) The t-distribution is symmetrical and has a mean of 0

9) The t statistic is used for hypothesis tests in situations where the population standard deviation (or variance) is unknown.

Answer 1

(1) FALSE

>> If the obtained sample data are in the critical region, then we could conclude that the data provide reject for the null hypothesis and support in the favour of the alternative hypothesis.

(2) TRUE

>> If a researcher is predicting that a treatment will increase scores, then the critical region for a directional test will be in the right-hand tail.

(3) TRUE

>> Whenever the statistical decision is to reject the null hypothesis, there is risk of a type I error, because type I error happen when we reject the null hypothesis but in fact the null hypothesis is true.

(4) FALSE

>> Type II error happen when we fail to reject the null hypothesis but in fact the null hypothesis is false. Hence, in a type II error, the experimenter concludes there is no evidence for an effect when in fact an effect exist.

(5) FALSE

>> Type I error only depends on the confidence level, it doesn't matter whatever the sample sizes are.

(6) TRUE

>> If the null-hypothesis is rejected using a one-tailed test, then it certainly would be rejected if the researcher used a two-tailed test, because the two tailed p-value is half of the one tailed p-value. Hence, it's obvious that the two tailed p-value is also less than the significance level.

(7) FALSE

>> Increasing the alpha level (for example from .01 to .05) will increase the size of the critical region.

(8) TRUE

>> Both the t-distribution and z-distribution are symmetrical and have mean of 0.

(9) TRUE

>> The t statistic is used for hypothesis tests in situations where the population standard deviation (or variance) is unknown. If the population standard deviation is known, we use z statistic.