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Just as observations can appear to show a trend that is more rapid than projected, as is perhaps the case with recent trends in Arctic sea ice extent, they can also appear to show the opposite type of discrepancy.
That type of discrepancy is true across Iraq, but seldom is it lived with such intensity in the same place, by the same soldiers, as here to the north of Baghdad.
Zhou et al. (2013) proposed a new type of discrepancy, mixture discrepancy (MD), and showed that MD may be a better uniformity measure than wrap-around L2-discrepancy and centered L2-discrepancy.
This type of discrepancy is even more apparent in different neurodegeneration-related sample subsets.
Another type of discrepancy, a time-delay effect, was observed in this study (Figure 4).
There are a couple possible explanations for this type of discrepancy.
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The type of discrepancies noted by Neumann (1999) included requests to justify the study design and statistical analysis, alterations to the measurement instrument, rewording of information sheets and the requirement to involve a medical practitioner as a sponsor.
This reanalysis that separated the types of discrepancy did not have power to adequately test each type individually.
Whether some types of discrepancy are particularly strong markers of trial report unreliability independently therefore remains uncertain.
For types of discrepancy that did not show a significant difference, the direction of the trend was in each case towards more discrepancies in the retracted trial reports.
In particular, three types of discrepancy arose significantly more frequently in retracted than unretracted reports: factual discrepancies (P=0.002), arithmetical errors (P=0.01), and missed P values (P=0.02).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com