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The article “Why most published research findings are false” elaborates on why most recent research is not reliable. Research findings are assessed based on the study power, biasness, and connection between variables under study. In case field studies are less than required, the chance of the findings being wrong is high. The various methodologies have proven the surge in false findings. The leading cause of the poor findings is the conclusive nature of the researchers, who tend to be content with only a single study with a level of statistical significance of less than 0.05 (Ioannidis, 2005). This is indicated by the increased rate of non-replication of research outcomes.
Bias can be described as the inclusion and combination of presentation factors, research designs, data, and analysis that lead to findings that ideally are wrong. However, it is essential to note that sometimes variability can be mistaken for bias as both have the same effect of making findings false. Prejudice has various forms, including selection, reporting, and data analysis bias (Ioannidis, 2005). The level of bias determines the probability of the findings being correct. An increase in bias will lead to a diminish in the probability of the findings being accurate. In some incidents, valid findings may be annulled due to reverse bias. This mainly results from an error in measurement or when the researcher fails to recognize the statistical significance.
The increased rate of false findings is due to the mentality of isolated discoveries. Instead of more independent teams researching to try and establish replication, most experiments are done and interpreted by single teams. Various corollaries can determine the probability of a finding being true. These corollaries consider each factor and assess their impact on the findings. According to Ohlson (2021) the corollaries include the following six:
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The smaller the studies conducted in a scientific field, the less likely the findings will be factual.
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The smaller the effect size in a scientific field, the less likely the findings are to be true.
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The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true.
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The greater the flexibility in designs, definitions, outcomes, and analytical models in a scientific field, the less likely the research finding are to be true.
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The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true.
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The hotter a scientific field with more scientific teams involved, the less likely research findings are to be true.
One major challenge in research is that it is impossible to determine the truth with 100 percent certainty. Because of this, the pure “gold” standard cannot be achieved. The accuracy of research findings may depend on the study design used and its effectiveness. If a random controlled trial is conducted well and powered adequately and with a 50 percent pre-test, it is more likely to yield better results that are reliable. Epidemiological studies that are less powered produce false results, primarily due to bias. Laboratory methods outcomes and reporting should be standardized to achieve quality and genuine findings. There is a need to change the scientific mentality used in research to solve the problem. This will aid in diminishing bias by enhancing standards and limiting prejudices.
References
Ioannidis, J. P. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. Web.
Ohlson, J. A. (2021). Researchers’ data analysis choices: An excess of false positives? Review of Accounting Studies, 1-19. Web.
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