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. Publishing only results that show a significant finding disturbs the balance of findings, and inserts bias in favor of positive results. The study of publication bias is an important topic in metascience.

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. A consequence of this is that researchers are unduly motivated to manipulate their practices to ensure that a statistically significant result is reported.

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. The nature of these issues and the problems that have been triggered, have been referred to as the 5 diseases that threaten science, which include: "significosis, an inordinate focus on statistically significant results; neophilia, an excessive appreciation for novelty; theorrhea, a mania for new theory; arigorium, a deficiency of rigor in theoretical and empirical work; and finally, disjunctivitis, a proclivity to produce large quantities of redundant, trivial, and incoherent works."

Attempts to identify unpublished studies often prove difficult or are unsatisfactory.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.

 

Note:   The above text is excerpted from the Wikipedia article Publication bias, which has been released under the GNU Free Documentation License.