Transcript Statistics for the Behavioral Sciences

Susan A. Nolan and Thomas E. Heinzen
Statistics for the Behavioral Sciences
Second Edition
Chapter 6:
The Normal Curve, Standardization, and z Scores
iClicker Questions
Copyright © 2012 by Worth Publishers
Chapter 6
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.
Chapter 6
(Answer)
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.
Chapter 6
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.
Chapter 6
(Answer)
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.
Chapter 6
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.
Chapter 6
(Answer)
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.
Chapter 6
4. When transforming raw scores into z scores, the formula is:
a)
(μ – X)
Z= ___________
Σ
b)
(X – μ)
Z= __________
σ
c)
(∑ – X)
Z= __________
Σ
(X – σ)
Z= _________
S
d)
Chapter 6
(Answer)
4. When transforming raw scores into z scores, the formula is:
a)
(μ – X)
Z= ___________
Σ
b)
(X – μ)
Z= __________
σ
c)
(∑ – X)
Z= __________
Σ
(X – σ)
Z= _________
S
d)
Chapter 6
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
Chapter 6
(Answer)
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
Chapter 6
6. The mean of a z distribution is always:
a)
b)
c)
d)
1.
0.
10.
100.
Chapter 6
6. The mean of a z distribution is always:
a)
b)
c)
d)
1.
0.
10.
100.
(Answer)
Chapter 6
7. A normal distribution of standardized scores is called
the:
a)
b)
c)
d)
standard normal distribution.
null distribution.
z distribution.
sample distribution.
Chapter 6
(Answer)
7. A normal distribution of standardized scores is called
the:
a)
b)
c)
d)
standard normal distribution.
null distribution.
z distribution.
sample distribution.
Chapter 6
8. The assertion that a distribution of sample means
approaches a normal curve as sample size increases is
called:
a)
b)
c)
d)
Bayes theorem.
the normal curve.
De Moivre’s theorem.
the central limit theorem.
Chapter 6
(Answer)
8. The assertion that a distribution of sample means
approaches a normal curve as sample size increases is
called:
a)
b)
c)
d)
Bayes theorem.
the normal curve.
De Moivre’s theorem.
the central limit theorem.
Chapter 6
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) All of the above are true.
Chapter 6
(Answer)
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) All of the above are true.
Chapter 6
10. The standard deviation of a distribution of means is
called the:
a)
b)
c)
d)
standard score.
standard error.
central limit theorem.
normal curve.
Chapter 6
(Answer)
10. The standard deviation of a distribution of means is
called the:
a)
b)
c)
d)
standard score.
standard error.
central limit theorem.
normal curve.
Chapter 6
11. Statisticians can use principles based on the normal
curve 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.
Chapter 6
(Answer)
11. Statisticians can use principles based on the normal
curve 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.