What are extreme scores? They are
scores far outside the norm for a variable or population, leading to the
conclusion they are not part of your true population and probably do not belong
in your analyses. A common operational definition for extreme scores is +/-3
standard deviations (SDs) from the mean.
Recall the standard normal
distribution of a population has 68.26% of the population between +1 and -1 SD
of the mean (see diagram: [34.13% between 0 to +1 SD] + [34.13% between 0 and
-1 SD] = 68.26%).
So 95.44% of the population should
fall between ±2 SD from the mean (34.13% + 34.13% + 13.59% +13.59% = 95.44%),
and 99.74% of the population should fall within ±3 SD of the mean. In other
words, the probability of randomly sampling an individual more than ±3 SD from
the mean in a normally distributed population is 0.26% (.0026), which gives
good justification for considering scores outside ±3 SD as suspect. The concern
is these extreme scores are not part of the population of interest in your
study.
Next time we will consider the
effects of extreme scores. Do you have an issue or a question that you would
like me to discuss in a future post? Would you like to be a guest writer? Send
me your ideas! leann.stadtlander@waldenu.edu
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