Chapter 5 Flashcards
The distributions that we have described so far are all empirical distributions—
that is, they are all based on
real data
The value of the theoretical normal
distribution lies in the fact that
t many empirical distributions that we study seem to
approximate it. t. We can often learn a lot about the characteristics of these empirical
distributions based on our knowledge of the theoretical normal distribution.
Normal distribution
n A bell-shaped and symmetrical theoretical distribution with the mean, the median,
and the mode all coinciding at its peak and with the frequencies gradually decreasing at both ends of the
curve.
what percent between mean and 1+, 2+ and 3+ resppectively
between the
mean and ±1 standard deviation, 68.26% of all the observations in the distribution occur;
between the mean and ±2 standard deviations, 95.46% of all observations in the
distribution occur; and between the mean and ±3 standard deviations, 99.72% of the
observations occu
. When we say that a
variable is “normally distributed,” we mean that the graphic display will
reveal an
approximately bell-shaped and symmetrical distribution closely resembling the idealized
model shown in Figure 5.1. This property makes it possible for us to describe many
empirical distributions based on our knowledge of the normal curve
As long as a
distribution is normal and we know the mean and the standard deviation, we can
determine the
proportion or percentage of cases that fall between any score and the mean.
This property provides an important interpretation for the standard deviation of empirical
distributions that are approximately normal. For such distributions, when we know the
mean and the standard deviation, we can determine
the percentage or proportion of scores
that are within any distance, measured in standard deviation units, from that distribution’s
mean.
The fixed
relationship between the distance from the mean and the areas under the curve applies only
to distributions that are normal or approximately normal.
z scores- what are they used to show
We can express the difference between any score in a distribution and the mean in terms of
standard scores,
A standard (Z) score is
the number of standard
deviations that a given raw score (or the observed score) is above or below the mean
A raw
score can be transformed into a Z score to find
how many standard deviations it is above or
below the mean.
To transform a raw score into a Z score, we
divide the difference between the score and the
mean by the standard deviation.
A Z score allows us to represent
t a raw score in terms of its relationship to the mean and to
the standard deviation of the distribution
The larger the Z score,
236
the larger the difference between
the score and the mean.
Standard normal distribution
n A normal distribution represented in standard (Z) scores, with mean = 0 and
standard deviation = 1.