文档介绍：Review of the Normal Distribution
•A distribution is a collection of scores
Lecture 5 (values) on a variable that is arranged in
order from lowest to highest value on the
horizontal (X) axis, and in terms of
frequency on the vertical (Y) axis. A
T-Test normal distribution, sometimes referred
to as a bell curve, has a distribution that
forms the shape of a bell. All you need to
know to plot the normal distribution is the
mean and standard deviation of the data.
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This lecture covers • A normal distribution with a mean of µ, and
a standard deviation of σ, is denoted as
N(µ, σ). If a set of scores has a
• Normal distribution distribution of N(15, 2), then we would say
it is a normal distribution with a mean of 15
• One-sample t-test and a standard deviation of 2. Normal
distributions do not all look alike; their
• Paired-sample t-test shape depends on the values of the mean
• Two-sample t-test and standard deviation. For a given mean,
a normal distribution may be tall and thin
(if σ is small), or short and flat (if σ is
large). See the figures in the next slide.
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• While normal distributions are all not the
same, they share an important
characteristic: a given standard deviation
from the mean always “cuts off” the same
proportion or percentage of scores in all
normal distributions.
• Specifically, one standard deviation above
and below the mean includes about 68%
of the scores; two (actually, about )
standard deviations above and below the
mean include 95 percent, and three
include more than percent of the
scores.
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These percentages are worth
Major characteristics
committing to memory: 68-95-
• It is symmetrical, meaning that the upper and
lower halves of the distribution of scores are
mirror images of each other.
• Second it is unimodal; the mean, median and
mode are all in the same place, in the center of
the dis