Presentation in the UCLA Chemistry & Biochemistry Department faculty luncheon series, May 2, 2011. Describes empirical information metrics for measuring the "information value" of an experiment either before you actually do it (experiment planning), or after (measuring how much information is contained in the empirical data, for improving our total prediction power). In addition to outlining the basic theory, presents some example applications (e.g. the information value of including a control experiment), and a real-world application to the computational design of a new genomics experiment, "phenotype sequencing", for identifying the genetic causes of a phenotype directly from sequencing of multiple independent mutants.