Download Chapter 6 iClicker Questions

Chapter 6 iClicker Questions
1. All of the following are true of the normal curve EXCEPT:
a) it is bell-shaped.
b) it is unimodal.
c) it has an inverted U shape.
d) it is symmetric.
2. A normal distribution of scores will more closely resemble a normal curve
as:
a) the sample size increases.
b) the sample size decreases.
c) more outliers are added to the sample.
d) scores are converted to z-scores.
3. A z score is defined as the:
a) mean score.
b) square of the mean score.
c) square root of the mean score divided by the mean.
d) number of standard deviations a particular score is from the mean.
4. When transforming raw scores into z scores, the formula to which we
refer is:
(μ – X)
a)
Z=
Σ
(X – μ)
b)
Z=
σ
(∑ – X)
c)
Z=
Σ
(X – σ)
d)
Z=
S
5. Matthew recently took an IQ test in which he scored an IQ of 120. If the
population’s mean IQ is 100 with a standard deviation of 15, what is
Matthew’s z score?
a) -2.6
b) 1.6
c) -2.3
d) 1.3
6. The mean of a z distribution is always:
a) 1.
b) 0.
c) 10.
d) 100.
7. A normal distribution of standardized scores is called the:
a) standard normal distribution.
b) null distribution.
c) z distribution.
d) sample distribution.
8. The assertion that a distribution of sample means approaches a normal
curve as sample size increases is called:
a) Bayes theorem.
b) the normal curve.
c) De Moivre’s theorem.
d) the central limit theorem.
9. How is a distribution of means different from a distribution of raw scores?
a) The distribution of means is more tightly packed.
b) The distribution of means has a greater standard deviation.
c) The distribution of means cannot be plotted on a graph.
d) The distribution of means is more spread out.
10. The standard deviation of a distribution of means is called the:
a) standard score.
b) standard error.
c) central limit theorem.
d) normal curve.
11. In chapter 6 of your textbook, the authors give examples of how the
normal curve can be used to:
a) catch cheaters.
b) encourage people to conform to expected behavior.
c) remove unwanted scores from the data set.
d) detect confounds in an experiment.