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.