A) The variability of scores in the population.
B) The 'flatness' of the distribution of sample scores.
C) The variability of sample estimates of a parameter.
D) The variability in scores in the sample.
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A) We can be 95% confident that the true difference between the population means falls between −0.08 and 0.15.
B) The probability is 0.95 that a significant difference between the population means lies between −0.08 and 0.15.
C) The probability is 0.05 that the true difference between the population means is between −0.08 and 0.15
D) The two populations cannot have the same means.
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A) A one-tailed hypothesis
B) A non-scientific statement
C) A two-tailed hypothesis
D) A null hypothesis
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A) large
B) small
C) small to medium
D) medium to large
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A) The standard deviation is calculated only from sample attributes.
B) The standard error is a measure of central tendency.
C) All of the above.
D) The standard error is calculated solely from sample attributes.
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A) The data we have typed into SPSS is different from the data collected.
B) We conclude that there is an effect in the population when in fact there is not.
C) We conclude that there is not an effect in the population when in fact there is.
D) We conclude that the test statistic is significant when in fact it is not.
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A) All of the above.
B) Descriptive statistics describe the data.
C) Descriptive statistics enable you to make decisions about your data, for example, is one group mean significantly different from the population mean?
D) Descriptive statistics enable you to draw inferences about your data, for example does one variable predict another variable?
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A) Yes you can, because the correlation coefficient is .5 (which is above .30) and falls within the boundaries of the confidence interval.
B) No you cannot, because the lower boundary of the confidence interval is .131, which is less than .30, and so the true correlation could be less than .30.
C) Yes you can, because the upper boundary of the confidence interval is above .30 we can be 95% confident that the true correlation will be above .30
D) No you cannot, because the sample size was too small.
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A) p is the probability of observing a test statistic at least as big as the one we have if there were no effect in the population (i.e., the null hypothesis were true) .
B) p is the probability that the results are due to chance, the probability that the null hypothesis (H0) is true.
C) p is the probability that the results are not due to chance, the probability that the null hypothesis (H0) is false.
D) p is the probability that the results would be replicated if the experiment was conducted a second time.
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