title: When predictions don't predict.
abstract: One of the most widespread abuses of statistical methods involves the misinterpretation of statistical significance after the application of multiple comparisons. This paper highlights the problem by demonstrating 'significance' when clearly none exists.
authors.affiliation: Reproductive Biology Unit, Royal Women's Hospital, Melbourne, Australia.
authors.forename: A L
authors.initials: AL
authors.lastname: Speirs
authors.forename: R H
authors.initials: RH
authors.lastname: Asch
authors.forename: S J
authors.initials: SJ
authors.lastname: Silber
mesh_terms: D005260:Female; D005307:Fertilization in Vitro; D006801:Humans; D011247:Pregnancy / Q000706:statistics & numerical data*; D011336:Probability