The “file drawer problem” and tolerance for null results

Abstract

For any given research area, one cannot tell how many studies have been con-ducted but never reported. The extreme view of the "file drawer problem " is that journals are filled with the 5 % of the studies that show Type I errors, while the file drawers are filled with the 95 % of the studies that show non-significant results. Quantitative procedures for computing the tolerance for filed and future null results are reported and illustrated, and the implications are discussed. Both behavioral researchers and statisti-cians have long suspected that the studies published in the behavioral sciences are a biased sample of the studies that are actually carried out (Bakan, 1967; McNemar, 1960; Smart, 1964; Sterling, 1959). The extreme view of this problem, the "file drawer prob-lem, " is that the journals are filled with the 5 % of the studies that show Type I errors, while the file drawers back at the lab are filled with the 95 % of the studies that show nonsignificant (e.g., p>.05) results. In the past there was very little one could do to assess the net effect of studies, tucked away in file drawers, that did not make the magic.05 level (Rosenthal & Gaito, 1963, 1964). Now, however, although no definitive solution to the problem is available, one can establish reasonable boundaries on the prob-lem and estimate the degree of damage to any research conclusion that could be done by the file drawer problem. This advance in our ability to cope with the file drawer is an outgrowth of the in-creasing interest of behavioral scientists in summarizing bodies of research literature sys-Preparation of this article was supported in part by the National Science Foundation. I would like to thank Judith A. Hall and Donald B. Rubin for their valuable improvements of an earlier version of this article. Requests for reprints should be sent to Rober