A test used to determine the statistical Significance of an observation. Two main types of error can occur:

- 1. A Type I Error occurs when a false negative result is obtained in terms of the Null Hypothesis
by obtaining a
*false positive measurement.* - 2. A Type II Error occurs when a false positive result is obtained in terms of the Null Hypothesis
by obtaining a
*false negative measurement.*

result | name |

true positive result | Sensitivity |

false negative result | 1-Sensitivity |

true negative result | Specificity |

false positive result | 1-Specificity |

Multiple-comparison corrections to statistical tests are used when several statistical tests are being performed simultaneously.
For example, let's suppose you were measuring leg length in eight different lizard species and wanted to see whether the
Means of any pair were different. Now, there are pairwise comparisons possible, so even if all of
the *population* means are equal, it's quite likely that at least one pair of sample means would differ significantly at the
5% level. An Alpha Value of 0.05 is therefore appropriate for each individual comparison, but not for the set of *all* comparisons.

In order to avoid a lot of spurious positives, the Alpha Value therefore needs to be lowered to account for the number
of comparisons being performed. This is a correction for multiple comparisons. There are *many* different ways to do
this. The simplest, and the most conservative, is the Bonferroni Correction. In practice, more people are more willing
to accept false positives (false rejection of Null Hypothesis) than false negatives (false acceptance of Null
Hypothesis), so less conservative comparisons are usually used.

© 1996-9

1999-05-26