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[B] A theory or hypothesis is falsifiable if it can be logically contradicted by an empirical test. Popper emphasized the asymmetry created by the relation of a universal law with basic observation statements [ C ] and contrasted falsifiability to the intuitively similar concept of verifiability that was then current in logical positivism .
Falsifiability or defeasibility, which means that counterexamples to the hypothesis are logically possible. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is a possibility of deciding whether it is true or false based on experimentation by anyone.
Such evidence is called a falsification of the hypothesis. However, under the theory of confirmation holism it is always possible to save a given hypothesis from falsification. This is so because any falsifying observation is embedded in a theoretical background, which can be modified in order to save the hypothesis.
A hypothesis stating implications, often called predictions, that are falsifiable via experiment is of central importance here, as not the hypothesis but its implications are what is tested. [133] Basically, scientists will look at the hypothetical consequences a (potential) theory holds and prove or disprove those instead of the theory itself.
The statistical proof is the Bayesian demonstration that one hypothesis has a higher (weak, strong, positive) likelihood. [13] There is considerable debate if the Bayesian method aligns with Karl Poppers method of proof of falsification, where some have suggested that "...there is no such thing as "accepting" hypotheses at all.
The defining characteristic of all scientific knowledge, including theories, is the ability to make falsifiable or testable predictions. [13] The relevance and specificity of those predictions determine how potentially useful the theory is. A would-be theory that makes no observable predictions is not a scientific theory at all.
This ensures that the hypothesis test maintains its specified false positive rate (provided that statistical assumptions are met). [ 35 ] The p -value is the probability that a test statistic which is at least as extreme as the one obtained would occur under the null hypothesis.
Bayesian probability (/ ˈ b eɪ z i ə n / BAY-zee-ən or / ˈ b eɪ ʒ ən / BAY-zhən) [1] is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation [2] representing a state of knowledge [3] or as quantification of a personal belief.