In statistics there are two fundamental types of error, labeled simply Type I and Type II errors
. In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (a "false positive"), while a type II error is the failure to reject a false null hypothesis (a "false negative").
Finally, in conditions where we want to take into account multiple states (i.e., we are concerned with both Type I and Type II errors
), serial structures are superior.
When the number of sample size is a lot, it is possible to maintain the level of both type I and type II errors
at low range; of course, it was not the purpose of this study.
One could argue that identifying and interpreting change triggers is equivalent to testing hypotheses: the researcher faces the risk of both Type I and Type II errors
(Boyd, Dess & Rasheed, 1993).
The researchers suggested that by prescribing the relationship between type I and type II errors
in auditing standards, the misstatement can be adjusted.
For these hypotheses, Type I and Type II errors
are given as follows:
parametric tests; statistical power; Type I and Type II errors
If we consider the agency's decision to take action to be comparable to the acceptance of a hypothesis, we can relate these two kinds of failures to the more familiar type I and type II errors
often studied in statistics.