Chapter
Conclusion
Inferential
statistics are used to make inferences about an
unknown variable based on known descriptions.
Making sure you have a solid understanding of
descriptive statistics plays an important role in
taking this data to the next step.
In this chapter we discussed basic
inferential procedures and the different scenarios
in which they are used.
A t-test is a simple procedure used to
compare the means of two groups.
An ANOVA allows us to compare multiple groups
on multiple independent variables and therefore can
look at both main and interaction effects within the
data set. The
factor analysis is used to find subgroups or factors
within a large distribution of scores.
A regression analysis uses the correlation in
order to make predictions about future or unknown
scores. And
finally, the meta analysis is used to combine
multiple studies into one larger study.
We also discussed
alternative and null hypothesis and the
determination of type I and type II errors.
These last two identify the significance of
any results section.
Remember, if the probability of error in your
study is greater than your accepted error, no matter
what the numbers look like, you must accept the null
hypothesis. In
this case, the difference that may appear in the raw
data is not significant enough to infer a difference
in the overall population.
Conversely,
if the accepted error is less than the probability
of error, you must reject the null hypothesis.
There is no gray area in this aspect as there
are only two outcomes of any inferential procedure:
reject or accept the null.
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