Reference no: EM13805602

1. The standard error of the mean can be calculated by dividing μ by the square root of the number of values in the distribution.

True

False

Question 2.2. The z- test requires an estimate of the population standard deviation.

True

False

Question 3.3. The independent t-test is based on which distribution?

The distribution of difference scores.

The distribution of sample means.

The distribution of error scores.

The distribution of z.

Question 4.4. How does variability in the distribution of sample means compare to variability in a population based on individual scores?

Samples tend to vary less than individual scores.

Samples exaggerate differences among scores.

Individual scores tend to be more stable over time than samples.

Sample means vary less than individual scores.

Question 5.5. Random samples always accurately reflect the population they were drawn from.

True

False

Question 6.6. What is the advantage of a one-tailed test over a two-tailed test?

Less data variability in the groups involved.

Smaller critical values indicate significance.

Rejecting at HO

= .05 involves less chance of error.

There are fewer calculations to make.

Question 7.7. What question does the z test answer?

Is the individual characteristic of the group?

Has there been a type I error?

Does the sample represent the population?

Are the data normal?

Question 8.8. What is the relationship between the power of a statistical test and decision errors?

Powerful tests minimize the risk of decision errors.

Powerful tests are more inclined to type II than type I errors.

Powerful tests compensate for decision errors with stronger effect sizes.

Powerful tests minimize type II errors.

Question 9.9. The test statistic follows the formula of difference divided by variability.

True

False

Question 10.10. The one-sample t-test differs from the z-test in which way?

There are no parameter values involved in a t-test.

The t-test is more sensitive to minor differences between sample and population.

With the t-test one can be confident of the normality of the data.

The t-test requires no <known> parameter standard error of the mean.