The Standard Normal Distribution
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Transcript The Standard Normal Distribution
THE STANDARD NORMAL DISTRIBUTION
a.k.a. “bell curve”
THE NORMAL DISTRIBUTION
If a characteristic is normally distributed in
a population, the distribution of scores
measuring that characteristic will form a
bell-shaped curve.
This assumes every member of the
population possesses some of the
characteristic, though in differing degrees.
examples: height, intelligence, self
esteem, blood pressure, marital
satisfaction, etc.
Researchers presume that scores on most
variables are distributed in a “normal”
fashion, unless shown to be otherwise
Including communication variables
THE NORMAL DISTRIBUTION
Only interval or ratio level data can be
graphed as a distribution of scores:
Examples: physiological measures,
ratings on a scale, height, weight,
age, etc.
Any data that can be plotted on a
histogram
Nominal and ordinal level data cannot
be graphed to show a distribution of
scores
nominal data is usually shown on a
frequency table, pie chart, or bar
chart
MORE ABOUT THE NORMAL DISTRIBUTION
Lower scores are found toward
the left-hand side of the curve.
Medium scores occupy the
middle portion of the curve
this is where most scores
congregate, since more
people are average or
typical than not
Higher scores are found
toward the right-hand side of
the curve
In theory, the “tails” of the
curve extend to infinity (e.g.
asymptotic)
lower
scores
medium
scores
higher
scores
MORE ABOUT THE NORMAL DISTRIBUTION
In a normal distribution, the
center point is the exact
middle of the distribution (the
“balance point”)
In a normal, symmetrical
distribution, the mean,
median, and mode all occupy
the same place
mean
median
mode
COMPARING GROUPS BASED ON THEIR
MEANS AND STANDARD DEVIATIONS
Note the height of the curve
does not reflect the size of the
mean, but rather the number
of scores congregated about
the mean
NON-NORMAL DISTRIBUTIONS
Kurtosis refers to how “flat” or
“peaked” a distribution is.
In a “flat” distribution scores
are spread out farther from
the mean
There is more variability in
scores, and a higher
standard deviation
In a “peaked” distribution
scores are bunched closer to
the mean
There is less variability in
scores, and a lower
standard deviation
kurtosis
KURTOSIS
Non-normal distributions may
be:
Leptokurtic (or peaked)
Scores are clustered
closer to the mean
Mesokurtic (normal, bell
shaped)
Platykurtic (flat)
Scored are spread out
farther from the mean
NON-NORMAL DISTRIBUTIONS
Skewness refers to how
nonsymmetrical or “lopsided” a distribution is.
If the tail extends toward
the right, a distribution is
positively skewed
If the tail extends toward
the left, a distribution is
negatively skewed
skewness
MORE ABUT SKEWNESS
In a positively skewed
distribution, the mean
is larger than the
median
In a negatively skewed
distribution, the mean
is smaller than the
median
Thus, if you know the
mean and median of a
distribution, you can tell
if it is skewed, and
“guesstimate” how
much.
NEGATIVELY SKEWED DISTRIBUTION
Only 2% of Americans
earned more than
$250,000 per year in
2005
STANDARD DEVIATIONS AND THE
NORMAL DISTRIBUTION
Statisticians have calculated
the proportion of the scores
that fall into any specific
region of the curve
For instance, 50% of the
scores are at or below the
mean, and 50% of the scores
are at or above the mean
50%
50%
STANDARD DEVIATIONS AND THE
NORMAL CURVE
Statisticians have
designated different
regions of the curve, based
on the number of standard
deviations from the mean
Each standard deviation
represents a different
proportion of the total area
under the curve
Most scores or
observations (approx. 68%)
fall within +/- one standard
deviation from the mean
68.26%
34.13% 34.13%
-3 SD
-2 SD
-1 SD +1 SD
+2 SD
+3 SD
STANDARD DEVIATIONS AND THE
NORMAL CURVE
Thus, the odds of a
particular score, or set
of scores, falling within
a particular region are
equal to the percentage
of the total area
occupied by that region
34.13%
34.13%
13.59%
13.59%
2.14%
-3 SD
2.14%
-2 SD
-1 SD +1 SD
68.26%
95.44%
99.72%%
+2 SD
+3 SD
THE 68-95-99 % BENCHMARKS
68.2% of all scores should
lie within 1 SD of the mean
95.4% of all scores should
fall within 2 SDs of the
mean
99.7% of all scores should
fall within 3 SDs of the
mean
PROBABILITY THEORY AND STATISTICAL
SIGNIFICANCE
The odds that a score or
measurement taken at
random will fall in a specific
region of the curve are the
same as the percentage of
the area represented by that
region.
Example: The odds that a
score taken at random will
fall in the red area are
roughly 68%.
random
score
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-2
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68.26%
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PROBABILITY THEORY AND STATISTICAL
SIGNIFICANCE
The probability of a random
or chance event happening in
any specific region of the
curve is also equal to the
percentage of the total area
represented by that region
the odds of a chance event
happening two standard
deviations beyond the mean
are approximately 4.28%, or
less than 5%
-3
-2
The odds of a random
or chance event
happening in this
region are 2.14%
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+1
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The odds of a random
or chance event
happening in this
region are 2.14%
PROBABILITY THEORY AND STATISTICAL
SIGNIFICANCE
When a researcher states
that his/her results are
significant at the p < .05
level, the researcher means
the results depart so much
from what would be expected
by chance that he/she is 95%
confident they could not have
been obtained by chance
alone.
The results are probably due
to the experimental
manipulation, and not due to
chance
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By chance
alone, results
should wind up
in either of
these two
regions less
than 5% of the
time
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PROBABILITY THEORY AND STATISTICAL
SIGNIFICANCE
When a researcher states
that his/her results are
significant at the p < .01
level, the researcher means
the results depart so much
from what would be expected
by chance alone, that he/she
is 99% confident they could
not have been obtained
merely by chance.
The results are probably due
to the experimental
manipulation and not to
chance
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By chance alone, results
should wind up in either
of these two regions less
than 1% of the time.
+3
PROBABILITY THEORY AND STATISTICAL
SIGNIFICANCE
When a researcher employs a
nondirectional hypothesis,
the researcher is expecting a
significant difference at
either “tail” of the curve.
When a researcher employs a
directional hypothesis, the
researcher expects a
significant difference at one
specific “tail” of the curve.
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Nondirectional hypothesis
either tail of the curve
Directional hypothesis
one tail or the other
PROBABILITY THEORY AND STATISTICAL
SIGNIFICANCE
The “control” group in an
experiment represents normalcy.
Scores for a “control” group are
expected to be typical, or
“average.”
The “treatment” group in an
experiment is exposed to a
manipulation or stimulus
condition.
Scores for a “treatment” group are
expected to be significantly
different from those of the control
group.
The researcher expects the
“treatment” group to be 2 std. dev.
beyond the mean of the control
group.
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The control group should
be in the middle of the
distribution
The treatment group is
expected to be 2 std. dev
beyond the mean