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| By Russell James
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Russell James
Community Contributor
Quizzes Created: 6 | Total Attempts: 1,027
Questions: 11 | Attempts: 107  Settings  • 1.

### When comparing two groups in a sample, an insignificant p-value indicates

• A.

This (sample) difference could have easily occurred even if the two (population) groups were the same

• B.

The two groups do not differ in the population

• C.

The two groups do not differ in the sample

• D.

This (sample) difference could have easily occurred even if the two (population) groups were the completely different

A. This (sample) difference could have easily occurred even if the two (population) groups were the same
Explanation
An insignificant p-value indicates that the observed difference between the two groups in the sample is not statistically significant. This means that the difference could have easily occurred by chance, even if the two population groups were actually the same. Therefore, the correct answer is that the two groups do not differ in the population.

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• 2.

### Which of the following is an unacceptable interpretation of a statistically non-significant variable?

• A.

We can’t confidently tell the effect of this item

• B.

We can’t tell if the population groups that differ based on this item also differ in the outcome variable

• C.

The population groups that differ based on this item do not differ in the outcome variable

• D.

This (sample) difference could have easily occurred even if the population showed no difference

C. The population groups that differ based on this item do not differ in the outcome variable
Explanation
An unacceptable interpretation of a statistically non-significant variable is that the population groups that differ based on this item do not differ in the outcome variable. This interpretation is incorrect because a statistically non-significant result does not necessarily mean that there is no difference between the groups. It simply means that the study did not find enough evidence to conclude that there is a significant difference.

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• 3.

### A 95% confidence interval indicates that

• A.

The true population value is within 95% of the range of this confidence interval

• B.

This confidence interval is within 95% of the true population

• C.

The mean of the sample is within 95% of the mean of the confidence interval

• D.

If you kept taking random samples, 95% of the time the sample value would appear inside the confidence interval associated with each sample

• E.

If you kept taking random samples, 95% of the time the true (population) value would appear inside the confidence interval associated with each sample

E. If you kept taking random samples, 95% of the time the true (population) value would appear inside the confidence interval associated with each sample
Explanation
A 95% confidence interval indicates that if you kept taking random samples, 95% of the time the true (population) value would appear inside the confidence interval associated with each sample. This means that there is a high probability that the true population value falls within the range of the confidence interval.

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• 4.

### If we analyzed 100 random variables with inferential statistics, how many of them would we expect to be statistically significantly associated with a random outcome?

• A.

All of them

• B.

None of them

• C.

• D.

• E.

Explanation
When analyzing 100 random variables with inferential statistics, we would expect only about 5 of them to be statistically significantly associated with a random outcome. This is because statistically significant associations are not common and usually occur by chance in a small proportion of cases. Therefore, it is unlikely that all or none of the variables would be statistically significant, and the number is typically around 5 in a random sample of 100 variables.

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• 5.

### If a researcher tests 100 variables to find two statistically significant factors, but then reports that he only tested those two variables, the associated p-values are not valid due to the statistical problem of

• A.

Multiple comparisons

• B.

Statistical significance

• C.

Lying bastards

• D.

Statistical magnitude

• E.

Correlation

A. Multiple comparisons
Explanation
When conducting multiple statistical tests, the probability of obtaining a significant result by chance increases. This is known as the problem of multiple comparisons. In this scenario, the researcher tested 100 variables but only reported the results of the two variables that were statistically significant. By doing so, the researcher is cherry-picking the results and not accounting for the increased likelihood of obtaining false positives. Therefore, the associated p-values are not valid due to the statistical problem of multiple comparisons.

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• 6.

### Why might a relationship of high significance but tiny magnitude not have any practical importance?

• A.

Because the relationship is likely due to random chance

• B.

Because even if the relationship exists, the effect of the relationship might be so small that we don’t care

• C.

Because the relationship is likely impaired by multiple comparison

• D.

Because highly significant relationships cannot actually have very small magnitude

• E.

Because practical importance requires statistical significance

B. Because even if the relationship exists, the effect of the relationship might be so small that we don’t care
Explanation
A relationship of high significance but tiny magnitude may not have any practical importance because even if the relationship exists, the effect of the relationship might be so small that it is not considered significant or impactful. In practical terms, the small magnitude of the relationship means that it does not have a noticeable or meaningful effect, leading to the lack of practical importance.

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• 7.

### Even if we don’t know the underlying statistical methodology, a statistically significant negative coefficient can usually tell all of the following EXCEPT,

• A.

The associated variable is negatively related to the outcome

• B.

The associated variable has an opposite relationship with the outcome as compared with another significant positive variable

• C.

The associated variable is probably more strongly negative in its association with the outcome than another variable with a much smaller negative coefficient where both variables have similar standard error

• D.

The associated variable causes the outcome

D. The associated variable causes the outcome
Explanation
A statistically significant negative coefficient can suggest that the associated variable is negatively related to the outcome, that it has an opposite relationship with the outcome compared to a significant positive variable, and that it is probably more strongly negative in its association with the outcome compared to another variable with a much smaller negative coefficient but similar standard error. However, it does not necessarily imply that the associated variable causes the outcome, as correlation does not imply causation.

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• 8.

### When is an odds ratio negative?

• A.

When in the sample, the variable of interest is higher when the outcome variable is lower

• B.

When in the sample, the variable of interest is lower when the outcome variable is higher

• C.

When in the sample, the variable of interest is higher when the outcome variable is higher

• D.

When in the sample, the variable of interest is lower when the outcome variable is lower

• E.

Never

E. Never
• 9.

### Which academic search engine will give you the largest number of documents related to your academic search?

• A.

• B.

Web of Science

• C.

Econ Lit

• D.

Blackboard

• E.

Psychtoolbox

Explanation
Google Scholar is the correct answer because it is a specialized search engine that focuses on academic research. It indexes a wide range of scholarly literature, including articles, theses, books, conference papers, and more. It provides access to a vast number of documents from various disciplines, making it an ideal choice for academic searches.

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• 10.

### Statistical significance tells us

• A.

The chance that the association found was due to an unusual random sample from a population in which there was no underlying association

• B.

The chance that the association found has practical significance

• C.

The chance that the association found was due to an unusual random sample from a population in which there was an underlying association

• D.

The magnitude of the underlying association

• E.

If there is a causal relationship between the significant factor and the measured outcome

A. The chance that the association found was due to an unusual random sample from a population in which there was no underlying association
Explanation
Statistical significance tells us the likelihood that the association found was not the result of a random sample from a population where there is no underlying association. In other words, it helps determine if the observed association is likely to be a true effect rather than a chance occurrence.

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• 11.

### A p-value of less than .05 (p<.05) means

• A.

There is greater than a 5% chance that the result was caused by an unusual random sample where there was no actual (population) difference

• B.

There is less than a .05% chance that the result was caused by an unusual random sample where there was no actual (population) difference

• C.

There is less than a .05% chance that the result was caused by an unusual random sample where there was no actual (population) difference

• D.

There is less than a 5% chance that the result was caused by an unusual random sample where there was no actual (population) difference

• E.

There is a causal relationship between the significant factor and the measured outcome

D. There is less than a 5% chance that the result was caused by an unusual random sample where there was no actual (population) difference
Explanation
A p-value of less than .05 (p

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