Combining information: model selection in meta-analysis and methods for combining correlated p-values

Statistical analyses always lead to results that include an imprecision to a degree. Combining information refers to quantitative methods that can synthesize information from multiple sources on the same topic to reach a more precise and general result. A challenging issue in combining information is that the individual results may be correlated to each other. For example, in ecology, outcomes from multiple species are dependent on each other due to their shared evolutionary history. This dissertation focuses on two fields in combining information, meta-analysis and combining p-values, and examines techniques that can incorporate the dependence among the correlated measurements into the method for combining information. The results show that, by doing so, it is possible to reduce the risk of incorrect results, such as false positives. Furthermore, this dissertation introduces an open-source software that implements methods for combining p-values and adjustment techniques to incorporate the correlations among them.