Select all that apply A two-factor non-replicated ANOVA design is often used because: Multiple select question. the non-replicated design increases power. repeated treatment observations can be impossible to collect. the researcher knows they will have no measurement variation and not need replication. the observations are expensive.
Added by Diane L.
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So this is false. - Repeated treatment observations can be impossible to collect: true, often due to constraints or rarity of conditions. - No measurement variation and no need for replication: false; replication is about accounting for variation, not about Show more…
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11. When there is a significant F test, Fisher's LSD is used to: a. find out which means are significantly different b. avoid calculating an ANOVA c. find out the amount of variability in our samples d. avoid calculating a repeated t-test e. two of the above are correct 12. An advantage of an experiment which uses a repeated-measures ANOVA is that it reduces the contribution of error variability due to a. individual differences b. the effect of the treatment c. experimenter error d. none of the above 13. Dr. Skillmaster wanted to see if a memory drug made a significant difference in exam scores, so she gave an exam to a random sample of 30 students, then gave them each the memory drug, and then gave them a similar test the next day. Which problem might this study have? a. heterogeneity of variance b. unequal sample sizes c. practice effects d. irrelevant matching criteria e. none of the above
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Which of the following are reasons why we might NOT use a repeated measures t? Group of answer choices It requires too many subjects. It is more likely to reject a null hypothesis than the design with difference subjects in the groups. It allows the correlation between trials to influence the results. Information the subjects pick up in early trials may influence their performance on later trials in ways that we don’t find helpful.
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Discerning whether an effect is the result of any among confounding factors is done by: removing a factor from the analysis and re-executing the experiment executing replications of the initial block using the modulus F-statistic executing a subsequent block of tests where the new block is a reflection of the original block
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