Guide to using the learning objectives

1. Define^{1 }** simple experiment**.
Define

2. Compare^{4} and contrast^{4
}the following:

a.
Experimental hypotheses and hypotheses
that do not postulate a cause-effect relationship.

b. Experimental hypothesis and null hypothesis

3. State^{1} what conclusions can
be drawn when

a.
The null hypothesis is rejected

b. The null hypothesis is not rejected

4. Distinguish^{4} between

a.
Independent variable and dependent variable

b. Control group and experimental group

5. Explain^{2} the implications
of the need to keep observations independent on

a.
How participants are selected, and

b. How participants are treated.

6. Explain^{2} why the need for
random ** assignment** means that some hypotheses cannot be tested in
a simple experiment.

7. Distinguish^{4} between what
statistically significant results mean and what they do not necessarily mean.

8. Explain^{2 }why null results
do not prove the null hypothesis.

**Pages 381-389**

9. Define^{1} ** Type I error**,
describe

10.
Define^{1}** Type
II error**. Define

11.
Explain^{2
}why reducing the extent to which random error affects your study will **not** reduce your risk of making a Type I
error.

12.
Explain^{2
}why reducing the extent to which random error affects your study will
reduce your risk of making a Type II error.

13.
Describe^{2}how
the risk of Type II errors can be reduced. In your answer, be sure to address
steps that will (a) reduce random error, (b) allow random error to balance out,
and (c) allow the effect to be detected even when there is a lot of random
error in your data.

**Pages 389-392**

14.
Explain^{2
}how steps you take to increase your experiment's power could harm your experiment's
external validity. Then, explain^{2} how steps you take to increase
your experiment's external validity could harm your experiment's power.

15.
Explain^{2
}how using placebo treatments and double-blind procedures could improve
the construct validity of a simple experiment.

16.
Explain^{2
}how steps you take to increase your experiment's power could harm your experiment's
construct validity. Then, explain^{2} how steps you take to increase
your experiment's construct validity could harm the experiment's power.

17.
Explain^{2
}how steps you take to make your experiment more ethical could harm the experiment's
power. Then, explain^{2} how steps you take to increase your
experiment's power could make your experiment less ethical.

18.
Rank^{6
}the following in how important they should be in designing a simple
experiment: construct validity, ethics, external validity, and power. Justify^{6
}your rankings.

**Pages 392-400**

19.
Defend^{4
}the following comment: "The average score of the experimental group is an
estimate of what the average score would have been had all your participants received
the treatment."

20.
Explain^{2}why
the following statement is true: "The mean for the treatment group could be
higher than the mean for the no-treatment group even if the treatment had no
effect."

21.
Examine^{4}why,
in the simple experiment, each of the following is true:

a.
random error causes scores within each
group to differ from one another.

b. random error may cause the experimental
group means to differ from the control group mean.

22.
The
means of your experimental and control groups differ. Outline^{3} the factors that a
statistical test would use to determine whether the difference between the
means is large enough to be due to the treatment rather than to chance alone.
Examine^{4} the role of the following in your answer:

a.
The size of the difference between your
two group means,

b. The amount of variability __within __each
of your groups, and

c.
The number of participants in each
of your groups.

23.
Choose^{1}which
of the following experiments illustrates a difference between the experimental
and control groups that is most likely to be due to more than chance alone.
Defend^{4} your choice.

**Experiment A** **Experiment
B**

__Control Experimental Control Experimental__

4 8 8 12

5 10 10 15

6 10 12 16

3 10 6 13

4 14 8 18

5 16 10 21

**Pages 400-406**

24.
State^{1}the
basic idea behind the *t* test.

25.
You
find the following on a computer printout of a *t* test analysis: "*df*
= 12, *t* = 5, *p* <.05, treatment group mean = 11, control group mean
= 1, standard error of the difference between means =2."

a.
Determine^{3} the number of scores
the computer analyzed.

b. Determine^{3} whether the results
were statistically significant.

c.
Show^{3 }the numerator (top
part) and the denominator (bottom part) of the *t* ratio.

d. Compute^{3} an index of effect
size (see ^{6} whether the treatment had
a large, medium, or small effect.

26.
Referring
to the concept of effect size, explain^{2} the difference between statistical
significance and practical significance (having an important effect).

**Pages 406-410**

27.
List^{1}the
two most essential assumptions that must be met to compute a meaningful *t*
test.

28.
List^{1}two
less serious assumptions of the *t* test. Then, explain^{2} why
these assumptions are usually not a threat to conducting a meaningful *t*
test. In your answer, be sure to refer to the central limit theorem.

29.
List^{1
}six questions to ask when results of your simple experiment are ** not** statistically significant.

30.
List^{1
}two questions to ask when results of your simple experiment are
significant.