Question 1 (1 point) Why must there be no interaction between treatment and bloc

Question

Question 1 (1 point)

Why must there be no interaction between treatment and blocking factors?

Question 1 options:

None of these options

Because if the interaction is significant then we cannot interpret the main effects, which defeats the whole purpose of including the blocking factor to begin with.

Because if the interaction is significant we run out of degrees of freedom to estimate the main effect

Because if the interaction is significant then we have to check for equal variance and normality – and these assumptions cannot be verified in a completely randomized block design

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Question 2 (1 point)

A blocking factor should be included in a study to reduce the uncertainty in the main effect estimates (of the treatment factor)

Question 2 options:

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Question 3 (1 point)

A paired sample t-test is the same as a complete block design with just one treatment factor that has two levels, and just one replicate (i.e. K=1).

Question 3 options:

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Question 4 (1 point)

If we want to control the family wise error rate when making pairwise comparisons of treatment means in a randomized complete block design we cannot use the Tukey-Kramer adjustment.

Question 4 options:

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Question 5 (1 point)

Including a blocking factor that does not have much of an effect on the response variable will severely hamper your ability to detect significant effects in other factors in the study

Question 5 options:

None of these options

Because if the interaction is significant then we cannot interpret the main effects, which defeats the whole purpose of including the blocking factor to begin with.

Because if the interaction is significant we run out of degrees of freedom to estimate the main effect

Because if the interaction is significant then we have to check for equal variance and normality – and these assumptions cannot be verified in a completely randomized block design

Explanation / Answer

# 1. Why must there be no interaction between treatment and blocking factors?

Answere: last option (Because if the interaction is significant then we have to check for equal variance and normality – and these assumptions cannot be verified in a completely randomized block design)

reson :

#2. A blocking factor should be included in a study to reduce the uncertainty in the main effect estimates (of the treatment factor)

Answer: True because to reduce the uncertainty in the main effect estimates we have to use block factor.

#3 .A paired sample t-test is the same as a complete block design with just one treatment factor that has two levels, and just one replicate (i.e. K=1).

Answer: TRUE complete block design is a generalization of a paired t-test.

#4 If we want to control the family wise error rate when making pairwise comparisons of treatment means in a randomized complete block design we cannot use the Tukey-Kramer adjustment.

Answer: TRUE  because the Tukey confidence intervals will be wider and the hypothesis tests less powerful for a given family wise error rate when making pairwise comparisons of treatment means in a randomized complete block designinsted of that we choose the Dunnett method.

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