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Statistics in Psychology
In descriptive, correlational and
experimental research, statistics
are tools that help us see &
interpret what the unaided eye
might miss…

The type of statistics computed for a
set of data depends on what type of
data is collected.
Nominal Data


Gender is an example of nominal data, which
is data that simply identifies categories.
Yes/No answers on surveys & class level in
school (senior, junior, etc.).
Ordinal Data

Identifies the order in which data falls in a set.
Any ranking of items, from one-hit wonders to
class rank to home-run sluggers, is done as
ordinal data.
Interval Data

Includes data that falls within a number line
that has a zero point. Weight has a real zero
point in that a weight of zero means no
weight (of course, zero weight means
something does not exist!) Height is another
example of interval data.
Ratio Data

Includes data that falls in a number line
where zero is just another number on the
line. For instance, a temperature of zero
degrees does not mean there is no
temperature. Test scores can also be ratio
data in that a score of zero usually doesn’t
mean “absence of knowledge.”
Measures of Central Tendency
~A score that represents a whole set of scores.
*Central tendency refers to how the data
measure the center of a set of data. The
mode, median & mean all point to where the
middle of the data should be.
*If the mode, median & mean are all the same
number, the graph of the data will look like a
normal curve. If they are different, the graph
will be skewed, or off center, in some way.
Positive & Negative Skew

Positive skew occurs when scores pull the
mean toward the higher end of the scores.
Hence the mean is more “positive” or greater
than the rest of the scores. Negative skew is
the opposite, with the mean being pulled
down toward the lower end of the scores.
Measures of Variation

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The single number offered in measures of central
tendency omits quite a bit of information. It helps to
know something about the variation in the data –
how similar or diverse the scores are.
Averages derived from scores with low variability are
more reliable than averages based on scores with
high variability. Thus, if a group of scores has a
smaller standard deviation, then you can draw
more stable conclusions from that data set.
Example:

If a set of test scores has a standard
deviation of 5, then everyone who took the
test scored similarly. If the scores have a
standard deviation of 50, then the test-takers’
scores were not very similar at all!
The Normal Curve

KNOWING THE PERCENTAGES OF
SCORES THAT FALL ONE, TWO & THREE
STANDARD DEVIATIONS FROM THE
MEAN IS IMPORTANT FOR THE AP
NATIONAL EXAM! KNOW THESE
PERCENTAGES WELL!!!!!!!!!!!!!~
Z-Score
A Z-Score is a statistical measurement of a
score's relationship to the mean in a group of
scores. A Z-score of 0 means the score is the
same as the mean. A Z-score can also be
positive or negative, indicating whether it is
above or below the mean and by how many
standard deviations.