In epidemiology, reporting bias is defined as "selective revealing or suppression of information" by subjects (for example about past medical history, smoking, sexual experiences). In artificial intelligence research, the term reporting bias is used to refer to people's tendency to under-report all the information available.

In empirical research, authors may be under-reporting unexpected or undesirable experimental results, attributing the results to sampling or measurement error, while being more trusting of expected or desirable results, though these may be subject to the same sources of error. In this context, reporting bias can eventually lead to a status quo where multiple investigators discover and discard the same results, and later experimenters justify their own reporting bias by observing that previous experimenters reported different results. Thus, each incident of reporting bias can make future incidents more likely.

Research can only contribute to knowledge if it is communicated from investigators to the community. The generally accepted primary means of communication is "full" publication of the study methods and results in an article published in a scientific journal. Sometimes, investigators choose to present their findings at a scientific meeting as well, either through an oral or poster presentation. These presentations are included as part of the scientific record as brief "abstracts" which may or may not be recorded in publicly accessible documents typically found in libraries or the World Wide Web.^{[citation needed]}

Sometimes, investigators fail to publish the results of entire studies. The Declaration of Helsinki and other consensus documents have outlined the ethical obligation to make results from clinical research publicly available.

Reporting bias occurs when the dissemination of research findings is influenced by the nature and direction of the results, for instance in systematic reviews. Positive results is a commonly used term to describe a study finding that one intervention is better than another.^{[citation needed]}

Various attempts have been made to overcome the effects of the reporting biases, including statistical adjustments to the results of published studies. None of these approaches has proved satisfactory, however, and there is increasing acceptance that reporting biases must be tackled by establishing registers of controlled trials and by promoting good publication practice. Until these problems have been addressed, estimates of the effects of treatments based on published evidence may be biased.

Litigation brought upon by consumers and health insurers against Pfizer for the fraudulent sales practices in marketing of the drug gabapentin in 2004 revealed a comprehensive publication strategy that employed elements of reporting bias. Spin was used to put emphasis on favorable findings that favored gabapentin, and also to explain away unfavorable findings towards the drug. In this case, favorable secondary outcomes became the focus over the original primary outcome, which was unfavorable. Other changes found in outcome reporting include the introduction of a new primary outcome, failure to distinguish between primary and secondary outcomes, and failure to report one or more protocol-defined primary outcomes.

The decision to publish certain findings in certain journals is another strategy. Trials with statistically significant findings were generally published in academic journals with higher circulation more often than trials with nonsignificant findings. Timing of publication results of trials was influenced, in that the company tried to optimize the timing between the release of two studies. Trials with nonsignificant findings were found to be published in a staggered fashion, as to not have two consecutive trials published without salient findings. Ghost authorship was also an issue, where professional medical writers who drafted the published reports were not properly acknowledged.