There is increasing concern that most current published research findings are false. The probability that a research
claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of
true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely
to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser
preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when
there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical
significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true.
Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias.
In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Objective: To heighten the public awareness of the exigencies of the reliability and validity of research
findings and to call for greater efforts towards improving the qualifications of decision-makers, managers, editors of
academic journals, as well as research workers, and towards better quality research and the greater scientific strength of
our country. Method: Three worrying trends in the international academic fields in recent years were assorted and
prevalent statistical errors of various descriptions in China's published research findings and papers were examined,
thereby alerting the professionals and the competent departments to threats and challenges facing China's research
endeavor and pointing at the necessity to give statistical work due attention. Results: Major factors responsible for
defective research designs and erroneous statistical analysis were pinned down and corrective suggestions were offered
for redressing the situation. Conclusion: Statistics is not only an effective and efficient tool for experimental investigation,
but also a necessary guarantee for the validity of research findings and papers.

We have briefly reviewed major causes of false or contradictive findings of published research
studies and biases in biomedical fields. Potential strategy and measures were proposed to reduce and control
false findings. R (pre-study odds) and PPV (positive predictive value) were considered to add extra but limited
values in assessing or interpreting research claims.

A precise definition of the discrete Hirsch index (h-index for short) is given and the notion of a Hirsch core
is introduced. The concept of a h-index is generalized to a real-valued setting. Definitions and examples are given of some
other generalizations of the h-index, namely the g-index and the A-index. It is concluded that the h-index and the g-index taken
together present a concise picture of a scientist,s achievements in terms of publications and citations.