On the optimistic performance evaluation of newly introduced bioinformatic methods

Most research articles presenting new data analysis methods claim that the new method performs better than existing methods, but the veracity of such statements is questionable. Our manuscript discusses and illustrates consequences of the optimistic bias occurring during the evaluation of novel data analysis methods, that is, all biases resulting from, for example, selection of datasets or competing methods, better ability to fix bugs in a preferred method, and selective reporting of method variants. We quantitatively investigate this bias using an example from epigenetic analysis: normalization methods for data generated by the Illumina HumanMethylation450K BeadChip microarray.

Automated summary

Many research articles claim that new data analysis methods outperform existing ones, but the veracity of such claims is questionable. This manuscript discusses the consequences of optimistic bias in evaluating novel data analysis methods, and quantitatively investigates this bias using an example from epigenetic analysis.