title: Inferential literacy for experimental high-throughput biology.
abstract: Many biologists believe that data analysis expertise lags behind the capacity for producing high-throughput data. One view within the bioinformatics community is that biological scientists need to develop algorithmic skills to meet the demands of the new technologies. In this article, we argue that the broader concept of inferential literacy, which includes understanding of data characteristics, experimental design and statistical analysis, in addition to computation, more adequately encompasses what is needed for efficient progress in high-throughput biology.
authors.affiliation: McGill University and Genome Quebec Innovation Centre, 740 Avenue du Docteur Penfield, Montreal, Quebec, Canada H3A 1A4.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Miron
authors.forename: Robert
authors.initials: R
authors.lastname: Nadon
mesh_terms: D000818:Animals; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000592:standards*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000592:standards*; D015203:Reproducibility of Results; D012984:Software