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Multiple Choice Quiz
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According to your text, inferential statistics are used to:
A)summarize data from single-subject designs.
B)infer the characteristics of a population from the characteristics of a sample.
C)help you decide whether you should replicate your study.
D)describe your data with a few numbers.
If you drew every possible sample of a given size from a population and calculated a mean for each sample, the distribution of those means is the:
A)sampling distribution of the mean.
B)standard error of the mean.
C)degrees of freedom of the mean.
D)central tendency of the mean.
The amount of variability in the expected sample means across a series of samples is estimated with the:
A)standard deviation.
B)population variance.
C)standard error of the mean.
D)interquartile range.
Then number of scores that can vary in a distribution with a known mean is the definition for:
A)a sampling distribution.
B)the standard error of the mean.
C)free variability.
D)degrees of freedom.
An assumption underlying parametric statistics is that:
A)sampling was done from a normally distributed population.
B)your data were measured on a nominal or an ordinal.
C)your data need not meet any strict requirements.
D)Both a and b
__________ statistics make no assumptions about your population.
If your independent variable has no effect on the dependent variable, the distributions representing the different groups in your experiment:
A)represent two distinct populations.
B)are independent samples drawn from the same population.
C)are probably positively skewed.
D)are probably negatively skewed.
The hypothesis that says that your sample means were drawn from the same population is the:
A)alternative hypothesis.
B)central limit hypothesis.
C)null hypothesis.
D)post hoc hypothesis.
The hypothesis that says that your sample means were drawn from different populations is the:
A)alternative hypothesis.
B)central limit hypothesis.
C)null hypothesis.
D)post hoc hypothesis.
If the probability that the difference between sample means could have resulted by sampling the same population is sufficiently small, then we say that the difference between means is:
A)not statistically significant.
B)statistically significant.
D)None of the above
Although inferential statistics are designed to help you minimize decision-making errors, errors are still possible. If you decided to reject the null hypothesis when in fact it was true, you are making a:
A)Type II error.
B)Type I error.
C)Type III error.
D)per-comparison error.
If you take steps to minimize a Type I error, then the probability of making a Type II error is:
B)also decreased.
D)cut in half.
By convention, alpha has been set at no larger than:
A)p < .10.
B)p < .05.
C)p < .025.
D)p < .01.
According to your text, if two samples come from the same population, then any difference between sample means is due to:
A)regression to the mean.
B)your independent variable.
C)your dependent variable.
D)sampling error.
To establish statistical significance you compare __________ with __________.
A)type I error; type II error
B)the sampling means; sampling error
C)the observed value of a statistic; critical value of a statistic
D)the degrees of freedom; critical value
According to your text, one-tailed tests should be used:
A)whenever you are unsure what kind of test to use.
B)in any situation in which you cannot predict the direction of an effect.
C)only if there is some compelling a priori reason not to use a two-tailed test.
D)when nonparametric statistics are used.
According to your text, the smaller the value of your alpha level:
A)the larger is the critical value of a statistic.
B)the smaller is the critical value of a statistic.
C)the larger is the observed value of a statistic.
D)the smaller is the observed value of a statistic.
Which of the following is not an assumption underlying parametric statistics?
A)The sampling distribution of the mean is normal.
B)Within groups variances are homogeneous.
C)Scores are sampled randomly from the population.
D)Independence of sample means.
The most appropriate statistical test for an experiment with two independent groups and the dependent variable measured on an interval scale is:
B)the t test for independent samples.
C)the one-sample z test.
D)a two-factor ANOVA.
For an experimental design that goes beyond two groups and a dependent variable measured on an interval scale, the best statistic is the:
B)t test for correlated samples.
C)Mann–Whitney U test.
D)chi-square test.
According to your text, total variation can be partitioned into:
A)independent and correlated variation.
B)type I and type II variation.
C)between-groups and within-groups variation.
D)None of the above
The F-ratio is a ratio between:
A)between-groups and within-groups variability.
B)between-groups variability and sampling error.
C)within-groups variability and the degrees of freedom.
D)None of the above
If you are contemplating doing many post hoc, unplanned comparisons, you must be concerned with:
A)per-comparison error.
B)beta errors.
C)familywise error.
D)probability funneling.
When you have specific preexperimental hypotheses, you can do _________ after finding a significant effect with an ANOVA.
A)planned comparisons
B)unplanned comparisons
C)ad hoc comparisons.
D)unweighted comparisons
If you have unequal sample sizes, you would use an unweighted means analysis if:
A)your experimental procedure caused the unequal sample sizes.
B)your experimental procedure did not cause the unequal sample sizes.
C)the size of the sample in one group did not exceed any of the others by more than three participants.
D)Both a and b
If you find a significant interaction using an ANOVA, which of the following would be true?
A)You ignore the interaction in favor of interpreting main effects.
B)There is a complex relationship between your independent variables.
C)You do not interpret the main effects.
D)Both a and b above
E)Both b and c above
If you have a design with both experimental and correlational variables, you would use which of the following statistics to analyze your data?
A)The analysis of covariance
B)The analysis of variance
C)The t-test for correlated samples
D)The Multivariate analysis of variance
Nonparametric tests:
A)are used only when your data do not meet the assumptions of parametric statistics.
B)are used if your data do not meet the assumptions of a parametric test, even if your data were scaled on an interval or ratio scale.
C)are used when your data are scaled on less than an interval scale.
D)Both a and b only
E)Both b and c only
The power of a statistical test refers to its:
A)ability to eliminate statistical errors.
B)ability to analyze data that violate the assumptions of the test.
C)ability to detect differences between means.
D)All of the above
The power of a statistical test is affected by:
A)sample size.
B)the alpha level chosen.
C)effect size.
D)all of the above
E)Both a and b only
If one finding is statistically significant at p .01 and a second at p .05, it would be logical to say that:
A)finding 1 is more significant than finding 2.
B)finding 2 is more significant than finding 1.
C)finding 1 and finding 2 are equally significant.
D)you can have greater confidence in rejecting the null hypothesis for finding 1 than finding 2.
A data transformation that changes the value of numbers, but not the scale of measurement are called:
A)nonlinear transformations.
B)geometric transformations.
C)linear transformations.
D)simple transformations.
A legitimate reason for transforming your data is:
A)to help a nonsignificant finding become significant.
B)when your data do not meet assumptions of a parametric statistic and no nonparametric alternative is available.
C)to reduce the effects of extraneous variables.
D)All of the above
If for some reason you cannot use inferential statistics, you may have to:
A)establish reliability through replication.
B)redo your experiment so that you can use inferential statistics.
C)simply “eyeball” your results to determine reliability.
D)ignore reliability issues and interpret your data anyway.

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