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Most of the research is false

preccupati There is increasing scientific discoveries that are false. The probability that a scientific statement is true depends on the capabilities and settings of the study, the number of studies on the same subject and, more importantly, the percentage of denied reports in each of those already tested in science.

being so scientific discovery tends to be less true:

• when the studies are few in a field;

• when there are few effects have been demonstrated;

• when and 'tested only a small number of reports is large while the number of possible relations;

• when there has been a great flexibility in design, definitions, results and methods analysis;

• when there is a strong economic interest or other behind the study or when there is no injury to the fund,

• when multiple groups are involved in a scientific work in the case of an assessment based statistics.

The simulations show that, according to the way studies are designed and set, in most cases, the findings tend to be more false than true. In several areas, in addition, the results can often be simply the result of accurate measurements of the prevailing prejudices.

Sometimes, studies are discredited by further discoveries that aim to create confusion and disappointment. You can see the creation of forms of stigma and controversy in a wide range of research. From clinical trials to traditional epidemiological studies, even the most modern molecular research. There is increasing concern that in modern research findings constitute a false majority. Not surprisingly, and you can prove it.

create the necessary conditions for false-positive findings

methodology Several experts have pointed out that the high percentage of non-replicability of certain scientific discoveries (ie the lack of confirmatory studies) is a consequence of a strategy of convenience and ill-founded, which aims to present as conclusive studies that rely only on a research project through a formal statistical analysis, generally with a percentage (p-value) of less than 0.05.

Science is not represented and summarized in the most appropriate percentage values, but unfortunately there is a widespread belief that the medical articles should only be interpreted in this way. The findings in these cases are defined as relationships that reach statistical significance as a causal, predictive information, risk factors or associations.

Partiality

First, we define as "bias" the combination - in the design, analysis and presentation - factors that tend to produce scientific discoveries that would otherwise not emerge. Consider how "u" the proportion of the tests proved that would not be considered scientific, but nonetheless end up being reported as such due to defects of bias below.

bias should not be confused with the random chance that some discoveries prove false to the event, although the study design, data, analysis and presentation are perfect. There may be a manipulation of the analysis both at the level of presentation of results. A typical form of manipulation is a selective presentation or distorted.

We can conclude, therefore, that the "u" does not depend on whether or not a real relationship (random). It is a foolish assumption, since it is generally impossible to know which reports are really true. (... ..) increasing the partial truths, the discovery is a real possibility that decreased considerably.

An example: science with low percentages in the pre-studies. Now, suppose that scientists manipulate their design, analysis and reporting relationships to overcome the threshold of 0.05 percent although this would not happen using the design and analysis that perfectly well as a comprehensive report of the results closely related to the original planning of the study.

One such manipulation can be performed for example

acting with the inclusion or exclusion eventful of certain elements in the group of patients or in the control group;

acting with a posteriori analysis of subgroups established for that purpose;

acting with the search for differences that were not specified at the beginning;

acting with changes in the definition of the disease or cases in the control group;

acting with various combinations of selective or distorted report of the results.

The software packages currently on the market of data mining claim to offer the option to hide the statistically significant data through the recovery of certain data (submerged). Even in the absence of initial bias, moreover, if ten independent research groups perform similar experiments around the world, if one of them finds a significant association on the basis of formal statistical probability that the discovery is true is only slightly higher than the probability that it is true that before embarking on extensive research .

The full article is here:

http://medicine.plosjournals.org/perlserv/?request=getdocument&doi=10.1371/journal.pmed.0020124

John PA Ioannidis is part of the Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina and the Institute for Clinical Research and for Health Policy Studies at the Department of Medicine Tufts New England Medical Center, School of Medicine of Tufts University in Boston, Massachusetts.