2. Measures of Central Tendency
Measures of Central Tendency describe
distributions based on the average
performance of a test score.
Measures of Central Tendency are typically
represented through the mean, median, and
mode.
3. Descriptors of Curves
Symmetry
Symmetrical – one side of the curve mirrors
the other
Asymmetrical – skew exists in the curve
4. Distribution Curves and Measures of
Central Tendency
In a symmetrical (normal) curve, the values for the
mean, mode, and median are identical.
The mean can be impacted by outlying scores.
In asymmetrical distributions, the median may be the
best measure of central tendency.
5. Measures of Variability
Variability – the degree to which scores differ from one another.
Measures of Variability – the degree to which scores differ from
the mean. There are several methods for measuring variability.
6. Skewness
– the degree to which the distribution of a curve is
asymmetrical.
Positive Skew - a distribution with an asymmetrical “tail”
extending out to the right.
Negative Skew - a distribution with an asymmetrical “tail”
extending out to the left.
7.
8. Interpretation: The distribution is positively skewed, that is the right tail of
the distribution is longer than the left tail. This suggests the presence of the
extreme values in the data set which are greater the median.
9.
10.
11. Kurtosis
– a statistic that reflects the peakedness or
flatness of a distribution relative to a normal
distribution.
12.
13. Types of kurtosis
Mesokurtic
A distribution
identical to the
normal
distribution
Leptokurtic
A distribution that
is more
peaked than
normal
Platykurtic
A distribution
that is less
peaked than
normal
