Question 1

Suppose you obtained a sample from a population for which the null hypothesis was true. On the basis of a t test, you failed to reject the null hypothesis. You have made a
A) Type I error
B) Type II error
C) correct decision
D) any of the other alternatives could be correct more information is needed.

Accepted Answer

Failing to reject the null hypothesis when it is true means you have made a correct decision, as there is no evidence to suggest the null hypothesis is false.

Question 2

Suppose you obtained a sample from a population different from the one specified by the null hypothesis. On the basis of a t test, you failed to reject the null hypothesis. You have made a
A) Type I error
B) Type II error
C) correct decision
D) any of the other alternatives could be more information is needed.

Accepted Answer

Failing to reject the null hypothesis when the sample comes from a population different from the one specified by the null hypothesis indicates a Type II error, which occurs when the null hypothesis is not rejected even though it is false.

Question 3

The reason for not reducing the $\alpha$ level to one in a million or even less is that
A) tables do not exist for such small levels
B)$\alpha$ would be too large
C) $\beta$ would be too large
D)$\beta$would be too small.

Accepted Answer

Reducing $\alpha$ to an extremely small level increases the risk of a Type II error ($\beta$), making it too large.

Question 4

An experimenter wanted to test the hypothesis that a particular sample came from a population with a mean of 10. The sample had a mean of 8 and the standard error of the mean was .80. In order to test the experimenter's hypothesis with a t test, you would also need to know
A) µ
B) $\bar { X }$
C) $s _ { X }$
D) N.

Accepted Answer

To conduct a t-test, you need to know the sample size (N) along with the sample mean ($\bar{X}$), the population mean ($\mu$), and the standard error of the mean (which is derived from the sample standard deviation ($s_{X}$) and N). The question already provides $\mu$, $\bar{X}$, and the standard error, so the missing piece of information is the sample size (N).

Question 5

The issue of the amount of difference between the mean of the population the sample is from and the mean of the null hypothesis population is the issue of
A) effect size
B) critical value
C) one- or two-tailed tests
D) significance level.

Accepted Answer

Effect size specifically refers to the magnitude of the difference between the population mean and the mean under the null hypothesis, indicating the practical significance of the findings.

Question 6

The issue of the amount of difference between the mean of the population the sample is from and the mean of the null hypothesis population is the issue of
A) hypothesis testing or confidence intervals
B) Type I and Type II errors
C) effect size
D) .05 or .01 $\alpha$ level.

Accepted Answer

The issue of the amount of difference between the mean of the population the sample is from and the mean of the null hypothesis population directly relates to the concept of effect size. Effect size quantifies the size of the difference between two groups, in this case, the actual population mean versus the hypothesized population mean under the null hypothesis.

Question 7

The issue of the amount of difference between a population with an unknown mean (which you sample from) and a null hypothesis population (µ₀= 8) is the issue of A) critical value B) effect size C) one- or two-tailed tests D) significance level.

Accepted Answer

The issue of the amount of difference between a population with an unknown mean and a null hypothesis population is referred to as the effect size. It quantifies the size of the difference between two populations under observation.

Question 8

The issue of the amount of difference between a population with an unknown mean (which you sample from) and a null hypothesis population (µ₀= 8) is the issue of A) hypothesis testing or confidence intervals B) Type I and Type II errors C) .05 or .01 $\alpha$ level D) effect size.

Accepted Answer

The issue described pertains to effect size, which measures the magnitude of the difference between a population mean (from which a sample is drawn) and a null hypothesis mean. This is not directly about the mechanics of hypothesis testing, error types, or significance levels, but rather about quantifying the strength or importance of the observed difference.

Question 9

When statisticians talk about effect size, they are referring to
A) the p value obtained from a test such as a t test
B) the difference between the mean of the population the sample is from and the mean of the null hypothesis population
C) whether an$\alpha$level of .05 or .01 was used
D) whether or not the test statistic was in the rejection region.

Accepted Answer

Effect size refers to the magnitude of the difference between groups or the strength of a relationship between variables, not just the statistical significance of the finding. It is often measured as the difference between the mean of the population from which the sample is drawn and the mean of the population under the null hypothesis, among other ways.

Question 10

When statisticians talk about effect size, they are referring to
A) the probability of a Type II error
B) whether the significance level was large or small
C) whether the$\alpha$ level was large or small
D) how different the sample mean was from the null hypothesis mean.

Accepted Answer

Effect size refers to the magnitude of the difference between the sample mean and the null hypothesis mean, indicating how substantial the observed effect is in the context of the research.

Question 11

An effect size index is most closely associated with which phrase below?
A) the $\alpha$ level chosen by the researcher
B) the probability of a Type II error
C) the size of the difference between the sample mean and the null hypothesis mean
D) the size of the rejection region.

Accepted Answer

An effect size index measures the magnitude of the difference between groups or variables, which is most closely related to the size of the difference between the sample mean and the null hypothesis mean.

Question 12

For a one-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 other alternatives are correct.

Accepted Answer

In the context of effect sizes, values of 0.80 and 0.90 are considered large according to Cohen's conventions. Similarly, values of 0.95 and 0.99 are also considered large. The value of 1.96 is often associated with statistical significance in a two-tailed test at the 0.05 level, but in the context of effect size, values above this threshold would also be considered large. Therefore, all the provided alternatives are correct examples of large effect sizes.

Question 13

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

Accepted Answer

In the context of effect sizes, a value of 0.80 is typically considered large according to Cohen's conventions. This means that the effect is strong or highly significant in a statistical sense. Cohen's d is a measure of effect size used in the context of a t-test, and values of 0.2, 0.5, and 0.8 correspond to small, medium, and large effect sizes, respectively.

Question 14

For a one-sample t test, an effect size that would qualify as small is
A) 0.05
B) 0.50
C) 0.80
D) all of the other alternatives are correct
E) none of the other alternatives are correct.

Accepted Answer

An effect size of 0.05 is considered small for a one-sample t-test, as it is much lower than the conventional threshold of 0.20 for a small effect size according to Cohen's conventions.

Question 15

For a one-sample t test, an effect size that would qualify as small is
A) any negative value
B) 0.20
C) 1.00
D) 1.96
E) 2.58.

Accepted Answer

A small effect size for a one-sample t test, according to Cohen's conventions, is 0.20. This is a standardized measure of the magnitude of the observed effect.

Question 16

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

Accepted Answer

In the context of effect sizes for a one-sample t test, an effect size (Cohen's d) of 0.80 is considered large according to Cohen's conventions. However, larger values such as 1.00 or 1.50 are also considered large, with the magnitude of the effect size increasing. Therefore, all the listed values are considered large effect sizes.

Question 17

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

Accepted Answer

The answer of For a one-sample t test, an effect...

Question 18

For a one-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 other alternatives are correct.

Accepted Answer

A small effect size in a one-sample t-test is indicated by a t-value that is close to zero, regardless of whether it is positive or negative.

Question 19

The statistic p tells you _______ and the statistic d tells you ________
A) the probability that the null hypothesis is true the size of the null hypothesis
B) the size of the null hypothesis the probability that the null hypothesis is true
C) the degree of separation between the sampled population and the null hypothesis population whether the population sampled from is different from the null hypothesis population
D) whether the population sampled from is different from the null hypothesis population the degree of separation between the sampled population and the null hypothesis population.

Accepted Answer

The statistic p (p-value) is used to determine whether the population sampled from is significantly different from the null hypothesis population, indicating the evidence against the null hypothesis. The statistic d (effect size) measures the degree of separation or difference between the sampled population and the null hypothesis population, quantifying the size of the effect.

Question 20

Knowing that p is small for a data set, you can conclude
A) that d is small
B) that d is large
C) nothing about the value of d.

Accepted Answer

Without additional context or definitions for "p" and "d," it's impossible to draw a conclusion about the relationship between the size of "p" and the value of "d." These variables could represent anything in different contexts (e.g., statistical measures, physical quantities), and their relationship would depend on the specific definitions and context provided.

Quiz 9: Hypothesis Testing and Effect Size: One-Sample Designs

Suppose you obtained a sample from a population for which the null hypothesis was true. On the basis of a t test, you failed to reject the null hypothesis. You have made a

Suppose you obtained a sample from a population different from the one specified by the null hypothesis. On the basis of a t test, you failed to reject the null hypothesis. You have made a

The reason for not reducing the $\alpha$ level to one in a million or even less is that

An experimenter wanted to test the hypothesis that a particular sample came from a population with a mean of 10. The sample had a mean of 8 and the standard error of the mean was .80. In order to test the experimenter's hypothesis with a t test, you would also need to know

The issue of the amount of difference between the mean of the population the sample is from and the mean of the null hypothesis population is the issue of

The issue of the amount of difference between the mean of the population the sample is from and the mean of the null hypothesis population is the issue of

The issue of the amount of difference between a population with an unknown mean (which you sample from) and a null hypothesis population (µ₀= 8) is the issue of

The issue of the amount of difference between a population with an unknown mean (which you sample from) and a null hypothesis population (µ₀= 8) is the issue of

When statisticians talk about effect size, they are referring to

When statisticians talk about effect size, they are referring to

An effect size index is most closely associated with which phrase below?

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

For a one-sample t test, an effect size that would qualify as large is

For a one-sample t test, an effect size that would qualify as small is

For a one-sample t test, an effect size that would qualify as small is

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

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

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

The statistic p tells you _ and the statistic d tells you __

Knowing that p is small for a data set, you can conclude

Quiz 9: Hypothesis Testing and Effect Size: One-Sample Designs

Suppose you obtained a sample from a population for which the null hypothesis was true. On the basis of a t test, you failed to reject the null hypothesis. You have made a

Suppose you obtained a sample from a population different from the one specified by the null hypothesis. On the basis of a t test, you failed to reject the null hypothesis. You have made a

The reason for not reducing the α\alphaα level to one in a million or even less is that

An experimenter wanted to test the hypothesis that a particular sample came from a population with a mean of 10. The sample had a mean of 8 and the standard error of the mean was .80. In order to test the experimenter's hypothesis with a t test, you would also need to know

The issue of the amount of difference between the mean of the population the sample is from and the mean of the null hypothesis population is the issue of

The issue of the amount of difference between the mean of the population the sample is from and the mean of the null hypothesis population is the issue of

The issue of the amount of difference between a population with an unknown mean (which you sample from) and a null hypothesis population (µ0 = 8) is the issue of

The issue of the amount of difference between a population with an unknown mean (which you sample from) and a null hypothesis population (µ0 = 8) is the issue of

When statisticians talk about effect size, they are referring to

When statisticians talk about effect size, they are referring to

An effect size index is most closely associated with which phrase below?

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

For a one-sample t test, an effect size that would qualify as large is

For a one-sample t test, an effect size that would qualify as small is

For a one-sample t test, an effect size that would qualify as small is

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

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

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

The statistic p tells you _______ and the statistic d tells you ________

Knowing that p is small for a data set, you can conclude

The reason for not reducing the $\alpha$ level to one in a million or even less is that

The issue of the amount of difference between a population with an unknown mean (which you sample from) and a null hypothesis population (µ₀= 8) is the issue of

The issue of the amount of difference between a population with an unknown mean (which you sample from) and a null hypothesis population (µ₀= 8) is the issue of

The statistic p tells you _ and the statistic d tells you __