Choose the alternative that best completes the stem of each question.

1		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.
2		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
3		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.
4		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.
5		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.
			C)	valid.
			D)	none of the above
6		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.
7		If you take steps to minimize a Type I error, then the probability of making a Type II error is
			A)	increased.
			B)	also decreased.
			C)	unaffected.
			D)	cut in half.
8		By convention, alpha has been set at no larger than p <
			A)	.10.
			B)	.05.
			C)	.025.
			D)	.01.
9		According to the text, one-tailed tests should be used
			A)	whenever you are unsure what kind of test to use.
			B)	in any situation where 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.
10		The most appropriate statistical test for an experiment with two independent groups and the dependent variable measured on an interval scale is
			A)	the chi-square.
			B)	the t test for independent samples.
			C)	the one-sample z test.
			D)	a two-factor ANOVA.
11		For an experimental design that goes beyond two groups and a dependent variable measured on an interval scale, the best statistic is the
			A)	ANOVA.
			B)	t test for correlated samples.
			C)	Mann—Whitney U test.
			D)	chi-square test.
12		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.
13		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
14		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 b and c
15		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
16		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
17		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.
18		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.
19		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
20		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.