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Publication bias can be contained through better-powered studies, enhanced research standards, and careful consideration of true and non-true relationships. [46] Better-powered studies refer to large studies that deliver definitive results or test major concepts and lead to low-bias meta-analysis.
A funnel plot is a scatterplot of treatment effect against a measure of study precision. 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 ...
Funding bias, also known as sponsorship bias, funding outcome bias, funding publication bias, and funding effect, is a tendency of a scientific study to support the interests of the study's financial sponsor. This phenomenon is recognized sufficiently that researchers undertake studies to examine bias in past published studies.
If sufficiently many scientists study a phenomenon, some will find statistically significant results by chance, and these are the experiments submitted for publication. Additionally, papers showing positive results may be more appealing to editors. [3] This problem is known as positive results bias, a type of publication bias. To combat this ...
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.
Spectrum bias arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the test. For example, a high prevalence of disease in a study population increases positive predictive values, which will cause a bias between the prediction values and the real ones. [4]
In addition to the main result, Ioannidis lists six corollaries for factors that can influence the reliability of published research. Research findings in a scientific field are less likely to be true, the smaller the studies conducted. the smaller the effect sizes. the greater the number and the lesser the selection of tested relationships.
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 ...