Difference between revisions of "Falsifiability"
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{{WikiEntry|key=Falsifiability|qCode=220888}}, is the '''opposite''' statement of [[non-falsifiable]], or [[irrefutable]]. A theory or hypothesis is [[falsifiable]] (or [[refutable]]) if it can be logically contradicted by an empirical test that can potentially be executed with existing technologies. | |||
=Excerpts from Wikipedia= | |||
One of the questions in [[wikipedia:scientific method|scientific method]] is: how does one move from [[wikipedia:observation|observation]]s to [[wikipedia:scientific law|scientific law]]s? This is the problem of induction. Suppose we want to put the hypothesis that all swans are white to the test. We come across a white swan. We cannot [[wikipedia:Validity (logic)|validly]] argue (or ''[[wikipedia:Inductive reasoning|induce]]'') from "here is a white swan" to "all swans are white"; doing so would require a [[wikipedia:logical fallacy|logical fallacy]] such as, for example, [[wikipedia:affirming the consequent|affirming the consequent]]. | |||
Popper's idea to solve this problem is that while it is impossible to verify that every swan is white, finding a single black swan shows that ''not'' every swan is white. We might tentatively accept the proposal that every swan is white, while looking out for examples of non-white swans that would show our conjecture to be false. Falsification uses the valid inference ''[[wikipedia:modus tollens|modus tollens]]'': if from a law <math>L</math> we logically deduce <math>Q</math>, but what is observed is <math>\neg Q</math>, we infer that the law <math>L</math> is false. For example, given the statement <math>L =</math> "all swans are white", we can deduce <math>Q =</math> "the specific swan here is white", but if what is observed is <math>\neg Q =</math> "the specific swan here is not white" (say black), then "all swans are white" is false. More accurately, the statement <math>Q</math> that can be deduced is broken into an initial condition and a prediction as in <math>C \Rightarrow P</math> in which <math>C =</math> "the thing here is a swan" and <math>P =</math> "the thing here is a white swan". If what is observed is C being true while P is false (formally, <math> C \wedge \neg P</math>), we can infer that the law is false. | |||
=Related to Data Science= | |||
If the data content includes information that indicates data sources, while the data set can be verified using publicly-known [[wikipedia:Tamperproofing|Tamperproofing]] techniques. | |||
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=References= | =References= | ||
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Latest revision as of 07:10, 14 June 2022
Falsifiability(Q220888), is the opposite statement of non-falsifiable, or irrefutable. A theory or hypothesis is falsifiable (or refutable) if it can be logically contradicted by an empirical test that can potentially be executed with existing technologies.
Excerpts from Wikipedia
One of the questions in scientific method is: how does one move from observations to scientific laws? This is the problem of induction. Suppose we want to put the hypothesis that all swans are white to the test. We come across a white swan. We cannot validly argue (or induce) from "here is a white swan" to "all swans are white"; doing so would require a logical fallacy such as, for example, affirming the consequent.
Popper's idea to solve this problem is that while it is impossible to verify that every swan is white, finding a single black swan shows that not every swan is white. We might tentatively accept the proposal that every swan is white, while looking out for examples of non-white swans that would show our conjecture to be false. Falsification uses the valid inference modus tollens: if from a law we logically deduce , but what is observed is , we infer that the law is false. For example, given the statement "all swans are white", we can deduce "the specific swan here is white", but if what is observed is "the specific swan here is not white" (say black), then "all swans are white" is false. More accurately, the statement that can be deduced is broken into an initial condition and a prediction as in in which "the thing here is a swan" and "the thing here is a white swan". If what is observed is C being true while P is false (formally, ), we can infer that the law is false.
Related to Data Science
If the data content includes information that indicates data sources, while the data set can be verified using publicly-known Tamperproofing techniques.
References