Publication bias

Publication bias is a type of bias that occurs in published academic research. It occurs when the outcome of an experiment or research study influences the decision whether to publish or otherwise distribute it. Publication bias matters because literature reviews regarding support for a hypothesis can be biased if the original literature is contaminated by publication bias. Publishing only results that show a significant finding disturbs the balance of findings.

Studies with significant results can be of the same standard as studies with a null result with respect to quality of execution and design. However, statistically significant results are three times more likely to be published than papers with null results.

Multiple factors contribute to publication bias. For instance, once a scientific finding is well established, it may become newsworthy to publish reliable papers that fail to reject the null hypothesis. It has been found that the most common reason for non-publication is simply that investigators decline to submit results, leading to non-response bias. Factors cited as underlying this effect include investigators assuming they must have made a mistake, failure to support a known finding, loss of interest in the topic, or anticipation that others will be uninterested in the null results.

Attempts to identify unpublished studies often prove difficult or are unsatisfactory. In an effort to combat this problem, some journals require that studies submitted for publication are pre-registered (registering a study prior to collection of data and analysis) with organizations like the Center for Open Science.

Other proposed strategies to detect and control for publication bias include p-curve analysis and disfavoring small and non-randomised studies because of their demonstrated high susceptibility to error and bias.

Publication bias occurs when the publication of research results depends not just on the quality of the research but also on the hypothesis tested, and the significance and direction of effects detected. The term was first used in 1959 by statistician Theodore Sterling to refer to fields in which "successful" research is more likely to be published. As a result, "the literature of such a field consists in substantial part of false conclusions resulting from type-I errors".

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