Perfect Study, Poor Evidence: Interpretation of Biases Preceding Study Design is a research paper published in Seminars in Hematology (2008). On theSindex it has a DataRank of 0.604. It has been cited 55 times.
In the interpretation of research evidence, data that have been accumulated in a specific isolated study are typically examined. However, important biases may precede the study design. A study may be misleading, useless, or even harmful, even though it seems to be perfectly designed, conducted, analyzed, and reported. Some biases pertain to setting the wider research agenda and include poor scientific relevance, minimal clinical utility, or failure to consider prior evidence (non-consideration of prior evidence, biased consideration of prior evidence, or consideration of biased prior evidence). Other biases reflect issues in setting the specific research questions: examples include straw man effects, avoidance of head-to-head comparisons, head-to-head comparisons bypassing demonstration of effectiveness, overpowered studies, unilateral aims (focusing on benefits and neglecting harms), and the approach of the industry towards research as bulk advertisement (including ghost management of the literature). The concerted presence of such biases may have a multiplicative, detrimental impact on the scientific literature. These issues should be considered carefully when interpreting research results.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.604
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.