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For example, explanatory power over all existing observations (criterion 3) is satisfied by no one theory at the moment. [ 10 ] Whatever might be the ultimate goals of some scientists, science, as it is currently practiced, depends on multiple overlapping descriptions of the world, each of which has a domain of applicability.
In biostatistics, strength of evidence is the strength of a conducted study that can be assessed in health care interventions, e.g. to identify effective health care programs and evaluate the quality of the research in health care.
The use of a sequence of experiments, where the design of each may depend on the results of previous experiments, including the possible decision to stop experimenting, is within the scope of sequential analysis, a field that was pioneered [13] by Abraham Wald in the context of sequential tests of statistical hypotheses. [14]
Such evidence is expected to be empirical evidence and interpretable in accordance with the scientific method. Standards for scientific evidence vary according to the field of inquiry, but the strength of scientific evidence is generally based on the results of statistical analysis and the strength of scientific controls. [citation needed]
In philosophy of science, strong inference is a model of scientific inquiry that emphasizes the need for alternative hypotheses, rather than a single hypothesis to avoid confirmation bias. The term "strong inference" was coined by John R. Platt , [ 1 ] a biophysicist at the University of Chicago .
Carl Sagan, seen here with a model of Viking lander, popularized the aphorism. "Extraordinary claims require extraordinary evidence" (sometimes shortened to ECREE), [1] also known as the Sagan standard, is an aphorism popularized by science communicator Carl Sagan. He used the phrase in his 1979 book Broca's Brain and the 1980 television ...
A strong research design yields valid answers to research questions while weak designs yield unreliable, imprecise or irrelevant answers. [ 1 ] Incorporated in the design of a research study will depend on the standpoint of the researcher over their beliefs in the nature of knowledge (see epistemology ) and reality (see ontology ), often shaped ...
In any case, users of a model need to understand the assumptions made that are pertinent to its validity for a given use. Building a model requires abstraction. Assumptions are used in modelling in order to specify the domain of application of the model. For example, the special theory of relativity assumes an inertial frame of reference.