Since
the purpose of this text is to help you to perform
and understand research more than it is to make you
an expert statistician, the inferential statistics
will be discussed in a somewhat abbreviated manner.Inferential statistics refer to the use of
current information regarding a sample of subjects
in order to (1) make assumptions about the
population at large and/or (2) make predictions
about what might happen in the future.The basic statistical methods explained in
the previous chapter are used a great deal in
inferential statistics, but the data is taken a step
further in order to generalize or predict.

We
can easily determine the mean of a known sample of
subjects by adding up all of their scores and
dividing by the number of subjects.The mean of a sample is therefore a known
variable.To
determine the mean of the population that has not
been testing or to predict the mean of a test that
has not yet been taken requires the researcher to
make assumptions because these variables are not
known to us.The
goal of inferential statistics is to do just that -
to take what is known and make assumptions or
inferences about what is not known.This chapter will focus on the basic
statistical procedures used for various types of
data and will conclude with an explanation of how
this data is used to estimate errors and make
inferences.

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