We’ve made it to the last paper in our #BucketListPapers series, and for that reason we are now going to tell you everything you have read is false. Not quite, but there are concerns that a large majority of the findings in modern research are false, therefore, when reading papers there must be an element of caution to check that the research is credible.
Ioannidis constructs a statistical model to indicate whether findings within a paper are a false positive result through different assumptions. It is important to note that “negative” research is sometimes just as important as research that works. Even though there seems to be less of a drive to publish papers when “negative” research occurs. These papers were not included in this research as only ones where there were claims of relationships were considered.
One of the main contributors is bias, whether willing or unknown. These biases can include the way in which the data and analysis has been handled and reported.
Six corollaries were stated that could impact the reliability of published research. These state that:
- smaller studies findings are less likely to be true
- the smaller the relationship, the less likely the findings are to be true
- the greater the number and the lesser the selection of tested relationships in the field, the less likely the findings are to be true
- the greater the flexibility in the whole study and analysis, the less likely the findings are to be true
- the greater the financial, other interests and prejudices in a field, the less likely the findings are to be true
- the hotter a scientific field (with more scientific teams involved), the less likely the findings are to be true
Even though there will never be a gold standard to publishing research findings Ioannidis outlines that we all must do more in order to improve the situation. The first is that we should have larger studies with low bias. The second is that each research team addressing the same research question should be considered equal and one should not be given more significance than another, as all evidence is equal. The final one is that we should improve our understanding of R values to stop chasing statistical significance and pre-studies consider what the chance are that a true relationship is being tested or a non-true relationship. Additionally, in order to prevent false findings being published all research results should be reproducible.
Why Most Published Research Findings Are False.
John P. A. Ioannidis
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