And, of course, the value returned for m 5 may be an unusual quantity measured accurately, revealing something startling or alarming or serendipitous about the phenomenon. She may work in a field which has a mechanistic definition of outlier values, and if such a definition exists it may be either well-justified or puzzlingly arbitrary. There may be sources of intrinsic heterogeneity within this measurement, and they may be known or unknown. She may have a great insight into the relevant problem space, or be encountering it for the first time. Our researcher may have strong prior beliefs, or some, or none. This sudden gatecrashing of the boundaries of expectation may require a decision about this data point’s acceptability to be made instantaneously, perhaps while a measurement device with a long set-up time is still running, or the decision may be an unhurried one made during later analysis. But if one cannot be located, researchers often rely on heuristic rather than objective rules. Of course, the first step would be to check if this aberrant value is inflated because of a clearly identifiable mechanistic factor-perhaps a hardware, software, or calculatory error. But, on the fifth measurement, her measurement apparatus returns a value of A − 6 B. If measurement error e is assumed to be negligible, she observes the majority of her observations fall between. Imagine a researcher taking individual measurements of M ( m 1, m 2, m 3, etc.), a hypothetical normally distributed quantity with a true mean of A and a natural variability with a true standard deviation of B. Discussions on these issues are many but in our opinion a crucial source of error is deserving of increased awareness: dubious conclusions due to selective redaction of data included in experimental observations.ġ.1. The reasons offered for the above are many, and are sometimes understood in terms of culpability-with ‘unwitting error’ on one end, ‘the wholesale fabrication of data’ on the other, and various questionable research practices and various methods of falsification somewhere in between. As many of these figures require dishonest actors to honestly report scientific misconduct, they are almost certainly underestimates of true prevalence. One recent overview suggested that 1–3% of scientists commit fraud, while questionable research practices occur in as much as 75% of published science. A National Institute of Health-funded study of early and mid-career scientists ( n = 3247) found 0.3% admitted to falsification of data in the prior year, 6% failing to present conflicting evidence, and 15.5% admitted to changing study design, methodology or results following pressure from funders. Inappropriate image manipulation was in 2006 estimated to occur in 1% of biological publications -a figure likely to have grown as technology improves. Why might this situation be so prevalent in biomedical literature? Unedifying as it seems, fraud and poor practice explain part of the picture. For landmark experiments in cancer research, the replication rate was an abysmal 11%. Another investigation of highly cited medical studies published between 19 found that while 45 originally claimed to find an effect, 16% were contradicted by further investigation, while another 16% reported effects stronger than subsequent studies allowed. In a sample of medical studies performed between 19, flaws were evident in 20% of medical studies. Psychology was perhaps the first discipline to report a ‘replication crisis’, but there is increasing evidence that biomedical science is facing a similar problem of an even greater magnitude. It is unknown how often redaction bias occurs in the broader literature, but given the risk of distortion to the literature involved, we suggest that it must be studiously avoided, and mitigated with approaches to counteract any potential malign effects to the research quality of medical science. We demonstrate that the removal of a surprisingly small number of data points can be used to dramatically alter a result. This is effectively truncation of a dataset by removing extreme data points, and we elucidate its potential to accidentally or deliberately engineer a spurious result in significance testing. Within this, we formally quantify the impact of inappropriate redaction beyond a threshold value in biomedical science. Many proximal causes of this irreproducibility have been identified, a major one being inappropriate statistical methods and analytical choices by investigators. Unreliable results impede empirical progress in medical science, ultimately putting patients at risk. A concerning amount of biomedical research is not reproducible.
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