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One effect of publication bias is sometimes called the file-drawer effect, or file-drawer problem. This term suggests that negative results, those that do not support the initial hypotheses of researchers are often "filed away" and go no further than the researchers' file drawers, leading to a bias in published research. [ 13 ]
It is used primarily as a visual aid for detecting bias or systematic heterogeneity. A symmetric inverted funnel shape arises from a ‘well-behaved’ data set, in which publication bias is unlikely. An asymmetric funnel indicates a relationship between treatment effect estimate and study precision.
Publication bias is a type of bias with regard to what academic research is likely to be published because of a tendency among researchers and journal editors to prefer some outcomes rather than others (e.g., results showing a significant finding), which leads to a problematic bias in the published literature. [139]
In educational measurement, bias is defined as "Systematic errors in test content, test administration, and/or scoring procedures that can cause some test takers to get either lower or higher scores than their true ability would merit." [16] The source of the bias is irrelevant to the trait the test is intended to measure.
For example, if a survey is conducted by a single individual, their own beliefs, biases, and perspectives can influence the responses of the participants. This "self reporting" is subjective, and limited because it is based on attitudes, values, and behaviours of the individual. [8] [9] Common source bias is also present in participant selection.
The publication or nonpublication of research findings, depend on the nature and direction of the results. Although medical writers have acknowledged the problem of reporting biases for over a century, [12] it was not until the second half of the 20th century that researchers began to investigate the sources and size of the problem of reporting biases.
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The term p-hacking (in reference to p-values) was coined in a 2014 paper by the three researchers behind the blog Data Colada, which has been focusing on uncovering such problems in social sciences research. [3] [4] [5] Data dredging is an example of disregarding the multiple comparisons problem. One form is when subgroups are compared without ...