Type I and Type II Errors
Since we are accepting some level of error in every
study, the possibility that our results are
erroneous are directly related to our acceptable
level of error.
If we set alpha at 0.05 we are saying that we
will accept 5% error, which means that if the study
were to be conducted 100 times, we would expect
significant results in 95 studies, and
non-significant results in 5 studies.
How do we then know that our study doesnt
fall in the 5% error category? We dont. Only
through replication can we get a better idea of
this.
There are two types of error that researchers are
concerned with: Type I and Type II. A Type I error occurs when the results of research show that
a difference exists but in reality there is no
difference. This
is directly related to alpha in that alpha was
likely set too high and therefore lowering the
amount of acceptable error would reduce the chances
of a Type I error.
Lowering the amount of acceptable error, however,
also increases the chances of a Type II error, which
refers to the acceptance of the null hypothesis when
in fact the alternative is true.
When there is a significant difference in the
population but we fail to find this difference, our
study is said to lack power.
Power, abbreviated with the upper case beta (b),
refers to a studys strength to find a difference
when a difference actually exists. In other words, the greater the chances of a Type I error,
the less likely a Type II error, and vice versa.
These two errors are summarized in Figure 9.1
Figure 9.1: Type I and Type II Errors
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