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Predictive power
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The predictive power of a scientific theory refers to its ability to generate testable predictions. Theories with strong predictive power are highly valued, because the predictions can often encourage the falsification of the theory. The concept of predictive power differs from explanatory and descriptive power (where phenomena that are already known are retrospectively explained by a given theory) in that it allows a prospective test of theoretical understanding.
Scientific ideas that do not confer any predictive power are considered at best "conjectures", or at worst "pseudoscience".
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Encyclopedia
The predictive power of a scientific theory refers to its ability to generate testable predictions. Theories with strong predictive power are highly valued, because the predictions can often encourage the falsification of the theory. The concept of predictive power differs from explanatory and descriptive power (where phenomena that are already known are retrospectively explained by a given theory) in that it allows a prospective test of theoretical understanding.
Scientific ideas that do not confer any predictive power are considered at best "conjectures", or at worst "pseudoscience". Because they cannot be tested or falsified in any way, there is no way to determine whether they are true or false, and so they do not gain the status of "scientific theory".
Theories whose "predictive power" presupposes technologies that are not currently possible constitute something of a grey area. For example, certain aspects of string theory have been labeled as predictive, but only through the use of machines that have not yet been built and in some cases may never be possible. Whether or not this sort of theory can or should be considered truly predictive is a matter of scientific and philosophical debate.
Examples
A classic example of the predictive power of a theory is the Discovery of Neptune as a result of predictions made by mathematicians John Couch Adams and Urbain Le Verrier, based on Newton's theory of gravity.
Other examples of predictive power of theories or models include Dmitri Mendeleev's use of his periodic table to predict previously undiscovered chemical elements and their properties (though largely correct, he misjudged the relative atomic masses of tellurium and iodine), and Charles Darwin's use of his knowledge of evolution by natural selection to predict that because there existed a plant (Angraecum) with a long spur in its flowers, a complementary animal with a 30 cm proboscis must also exist to feed on and pollinate it (twenty years after his death, a form of hawk moth was found which did just that).
Another example of predictive power is the prediction of Einstein's General Theory of Relativity that the path of light would bend in the presence of a strong gravitational field. This was experimentally verified by an expedition to Sobral in Brazil and the Atlantic island of Príncipe to measure star positions during the solar eclipse of May 29, 1919, when observations made by the astrophysicist Arthur Eddington seemed to confirm Einstein's predictions. Although the measurements have been criticized by some as utilizing flawed methodology, modern reanalysis of the data suggests that Eddington's analysis of the data was accurate. Later, more precise measurements taken by radio interferometry confirmed the predictions to a high degree of accuracy.
Applications
The predictive power of a theory is closely related to applications.
General relativity not only predicts the bending of light, but also predicts several other phenomena. Recently, the calculation of proper time of satellites has been a successfully-measured prediction, now incorporated into the method used to calculate positions via GPS.
If a theory has no predictive power, it cannot be used for applications.
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