The phrase "a rigorous conclusion" is correct and usable in written English.
It can be used when discussing the results of a thorough and careful analysis or argument that leads to a definitive outcome.
Example: "After analyzing the data from multiple experiments, we arrived at a rigorous conclusion that supports our hypothesis."
Alternatives: "a thorough conclusion" or "a well-founded conclusion".
Methodologically rigorous conclusions would require a much larger study, with more explicit control of variables such as renal function, use of inotropes and baseline-dependent and time-dependent variables (age, gender and admission diagnosis) included in a rigorous multivariable analysis.
Papers will be included if they are: Systematic reviews: where relevant literature has been identified by means of structured search of bibliographic and other databases; where transparent methodological criteria are used to exclude papers that do not meet an explicit methodological benchmark, and which presents rigorous conclusions about outcomes.
Systematic reviews: where relevant literature has been identified by means of structured search of bibliographic and other databases; where transparent methodological criteria are used to exclude papers that do not meet an explicit methodological benchmark, and which presents rigorous conclusions about outcomes.
Yet, in order to link and analyze all railway models, B machines will constitute a common destination point that allows us to arrive at rigorous conclusions.
Although careful analyses continue to be required for rigorous conclusions about such DNA damage, diversification of a cancer genome after constricted migration is now clear in vitro.
Thus, rigorous conclusions of for example, loss of activity or hyperactivation of CLEAs respect to native enzyme, can be drawn.
Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test.
The aim of this paper was to demonstrate the benefit of volunteer computing in computational ecology to systematically browse the parameter space and analyze the simulation results in order to draw rigorous conclusions.
More refined null expectations and adequate replication are needed to allow more rigorous conclusions.
But this promise is likely to be realized only after a period of relatively expensive and deliberate test validation and generation of the massive reference databases needed to make rigorous conclusions about test results.
