Question 1

For a two-sample t test, which of the following pairs of values qualify as large effect sizes?
A) 0.80 and 0.90
B) 0.95 and 0.99
C) above 1.96
D) all of the descriptive alternatives are correct.

Accepted Answer

Effect size values of 0.80 and 0.90 are considered large according to Cohen's conventions, and values above 1.96 typically indicate a very strong effect size, especially in the context of standard deviations or z-scores.

Question 2

For a two-sample t test, an effect size that qualifies as large is
A) 0.05
B) 0.20
C) 0.80
D) all of the descriptive alternatives are correct
E) none of the descriptive alternatives are correct.

Accepted Answer

In the context of a two-sample t-test, an effect size of 0.80 is considered large according to Cohen's conventions. Cohen's d is a measure of effect size used to indicate the standardized difference between two means. Values of 0.2, 0.5, and 0.8 are typically considered small, medium, and large effect sizes, respectively.

Question 3

For a two-sample t test, an effect size that qualifies as small is
A) 0.05
B) 0.20
C) both of the descriptive alternatives are correct
D) neither of the descriptive alternatives is correct.

Accepted Answer

Effect sizes of 0.20 and below are considered small.

Question 4

For a two-sample t test, an effect size that qualifies as small is
A) less than 0
B) 0.20
C) 1.00
D) all of the descriptive alternatives are correct.

Accepted Answer

An effect size of 0.20 is considered small according to Cohen's conventions for interpreting effect sizes in the context of a two-sample t-test.

Question 5

For a two-sample t test, an effect size that is considered large is
A) 0.80
B) 1.00
C) 1.50
D) all of the descriptive alternatives are correct
E) none of the descriptive alternatives are correct.

Accepted Answer

In the context of Cohen's d, which is commonly used to measure effect size in a two-sample t-test, an effect size of 0.80 is considered large. However, larger values such as 1.00 or 1.50 would also be considered large, indicating a stronger effect. Therefore, all the given options (0.80, 1.00, 1.50) represent large effect sizes.

Question 6

For a two-sample t test, an effect size that is considered small is
A) 0.05
B) 0.10
C) 0.20
D) all of the descriptive alternatives are correct
E) none of the descriptive alternatives are correct.

Accepted Answer

An effect size of 0.20 is generally considered small according to Cohen's conventions for interpreting effect sizes in the context of a two-sample t-test.

Question 7

For a two-sample t test, which of the following would be considered a small effect size?
A) any negative number
B) any negative number that is close to zero
C) numbers that are close to 1.96
D) none of the descriptive alternatives are correct.

Accepted Answer

A small effect size in a two-sample t-test is typically indicated by a number close to zero, whether positive or negative, as it suggests a small difference between the two groups being compared.

Question 8

Which of the following answers has effect size indexes that are considered small?
A) 0.01 and 0.05
B) 0.10 and 0.20
C) both of the descriptive alternatives are correct
D) neither of the descriptive alternatives is correct.

Accepted Answer

Both A) 0.01 and 0.05 and B) 0.10 and 0.20 represent ranges of effect sizes that can be considered small in various contexts, such as Cohen's conventions for effect size interpretation.

Question 9

An effect size index that is considered small is
A) any negative value
B) 0.20
C) 1.00
D) 1.96
E) 2.58.

Accepted Answer

An effect size index of 0.20 is considered small according to Cohen's conventions for interpreting effect sizes. Cohen defined small, medium, and large effect sizes for different statistical measures, with 0.20 typically representing a small effect size.

Question 10

Which of the following would qualify as a small effect size index?
A) a negative number
B) 1.96
C) 2.00 to 3.00
D) none of the descriptive alternatives are correct.

Accepted Answer

Effect size indices are typically positive values that indicate the magnitude of a difference or relationship, with smaller numbers (close to 0 but greater than 0) indicating a small effect. Negative numbers are not typically used to describe the size of an effect, and values like 1.96 or in the range of 2.00 to 3.00 would generally be considered large effect sizes in many contexts. Therefore, none of the provided alternatives correctly describe a small effect size index.

Question 11

Which of the following is important in determining whether you will reject the null hypothesis?
A) the size of the rejection region
B) difference between the two population means
C) the size of the sample size
D) all of the descriptive alternatives are correct.

Accepted Answer

All of the options listed are important in determining whether you will reject the null hypothesis. The size of the rejection region determines the critical value(s) for the test statistic, the difference between the two population means is directly related to the test statistic used in hypothesis testing, and the sample size affects the standard error and thus the test statistic.

Question 12

Your text says that the best way to increase the power of statistical tests is to
A) adopt a large$\alpha$ level
B) do your arithmetic twice
C) use large N's
D) all of the descriptive alternatives are correct.

Accepted Answer

The best way to increase the power of a statistical test is to use large sample sizes (denoted as "N"). Larger sample sizes reduce the standard error, making it easier to detect a true effect if one exists. Choices A and B do not directly increase the power of statistical tests.

Question 13

1 -$\beta$is the of a statistical test.
A) probability of $\alpha$
B) power
C) both of the descriptive alternatives are correct
D) neither of the descriptive alternatives are correct.

Accepted Answer

The answer of 1 -$\beta$is the of a statistical test.
A)...

Question 14

In which of the following would an increase from a smaller number to a larger number produce a more powerful statistical test?
A) actual difference between population means
B) alpha
C) both of the descriptive alternatives are correct
D) neither of the descriptive alternatives is correct.

Accepted Answer

An increase in the actual difference between population means would lead to a more powerful statistical test because it would make the effect size larger, making it easier to detect a true effect. Similarly, increasing alpha (the significance level) would also make a statistical test more powerful by increasing the likelihood of rejecting the null hypothesis, albeit at the cost of potentially increasing the Type I error rate. Therefore, both A and B are correct.

Question 15

In which of the following would an increase from a smaller number to a larger number produce a more powerful statistical test?
A) $\alpha$
B) $\beta$
C) both of the descriptive alternatives are correct
D) neither of the descriptive alternatives is correct.

Accepted Answer

Increasing the significance level $\alpha$ increases the power of a statistical test, as it reduces the probability of a Type II error ($\beta$).

Question 16

In which of the following would an increase from a smaller number to a larger number produce a more powerful statistical test?
A) $\beta$
B) N
C) both of the descriptive alternatives are correct
D) neither of the descriptive alternatives is correct.

Accepted Answer

An increase in sample size (N) leads to a more powerful statistical test because it reduces the standard error, making it easier to detect a true effect if one exists.

Question 17

As________ and____________ increase, a statistical test becomes more powerful.
A) actual difference between population means, the standard error of a difference
B) sample variability, alpha
C) sample size, beta
D) none of the descriptive alternatives are correct.

Accepted Answer

None of the given options correctly describe the factors that increase the power of a statistical test. Power increases with larger sample sizes and larger actual differences between population means, but these are not correctly paired in the options.

Question 18

You can increase the power of a statistical test by increasing
A) $\alpha$
B) sample variability
C) both of the descriptive alternatives are correct
D) neither of the descriptive alternatives are correct.

Accepted Answer

Increasing $\alpha$ (the significance level) increases the power of a statistical test because it increases the probability of rejecting the null hypothesis when it is false.

Question 19

Researchers control the probability of a Type I error in their experiment by setting the value of
A)$\alpha$
B) $\beta$
C) both of the descriptive alternatives are correct
D) neither of the descriptive alternatives are correct

Accepted Answer

The probability of a Type I error, which is rejecting a true null hypothesis, is controlled by setting the significance level $\alpha$.

Question 20

The 95 percent confidence interval about a mean difference was -3.0 minutes to 6.5 minutes. The null hypothesis that the two population means are equal
A) can be rejected at the .05 level
B) can be rejected at the .01 level
C) can be rejected at both the .05 and the .01 level
D) should be retained.

Accepted Answer

Since the 95% confidence interval includes 0 (ranging from -3.0 to 6.5), it indicates that there is no significant difference between the two means at the .05 level, thus the null hypothesis should be retained.

Quiz 10: Hypothesis Testing, Effect Size, and and Confidence Intervals: Two-Sample Designs

For a two-sample t test, which of the following pairs of values qualify as large effect sizes?

For a two-sample t test, an effect size that qualifies as large is

For a two-sample t test, an effect size that qualifies as small is

For a two-sample t test, an effect size that qualifies as small is

For a two-sample t test, an effect size that is considered large is

For a two-sample t test, an effect size that is considered small is

For a two-sample t test, which of the following would be considered a small effect size?

Which of the following answers has effect size indexes that are considered small?

An effect size index that is considered small is

Which of the following would qualify as a small effect size index?

Which of the following is important in determining whether you will reject the null hypothesis?

Your text says that the best way to increase the power of statistical tests is to

1 - $\beta$ is the of a statistical test.

In which of the following would an increase from a smaller number to a larger number produce a more powerful statistical test?

In which of the following would an increase from a smaller number to a larger number produce a more powerful statistical test?

In which of the following would an increase from a smaller number to a larger number produce a more powerful statistical test?

As and____ increase, a statistical test becomes more powerful.

You can increase the power of a statistical test by increasing

Researchers control the probability of a Type I error in their experiment by setting the value of

The 95 percent confidence interval about a mean difference was -3.0 minutes to 6.5 minutes. The null hypothesis that the two population means are equal

Quiz 10: Hypothesis Testing, Effect Size, and and Confidence Intervals: Two-Sample Designs

For a two-sample t test, which of the following pairs of values qualify as large effect sizes?

For a two-sample t test, an effect size that qualifies as large is

For a two-sample t test, an effect size that qualifies as small is

For a two-sample t test, an effect size that qualifies as small is

For a two-sample t test, an effect size that is considered large is

For a two-sample t test, an effect size that is considered small is

For a two-sample t test, which of the following would be considered a small effect size?

Which of the following answers has effect size indexes that are considered small?

An effect size index that is considered small is

Which of the following would qualify as a small effect size index?

Which of the following is important in determining whether you will reject the null hypothesis?

Your text says that the best way to increase the power of statistical tests is to

1 - β\betaβ is the of a statistical test.

In which of the following would an increase from a smaller number to a larger number produce a more powerful statistical test?

In which of the following would an increase from a smaller number to a larger number produce a more powerful statistical test?

In which of the following would an increase from a smaller number to a larger number produce a more powerful statistical test?

As________ and____________ increase, a statistical test becomes more powerful.

You can increase the power of a statistical test by increasing

Researchers control the probability of a Type I error in their experiment by setting the value of

The 95 percent confidence interval about a mean difference was -3.0 minutes to 6.5 minutes. The null hypothesis that the two population means are equal

1 - $\beta$ is the of a statistical test.

As and____ increase, a statistical test becomes more powerful.