The data presented must be evaluated as either being the result of necessity, chance, a combination thereof, or design (intelligent causation). How may one infer design? As William Dembski advocates in his work, The Design Inference, there must be a relay of specified complex information. In the 1940’s, Claude Shannon at Bell Laboratories developed a mathematical theory of information. The information-carrying capacity of a sequence of a specific length can then be calculated using the expression I=-log2p. When this formula is applied to genetic sequence probability formulas the information being conveyed is more than mere Shannon information. The word information in this theory is used in a special mathematical sense that must not be confused with its ordinary usage. In particular, information must not be confused with meaning.
Since the late 1950’s, biologists have equated the “precise determination of sequence” with the property “specificity” or “specification.” Biologists have defined specificity tacitly as “necessary to achieving or maintaining function.” They have determined that DNA base sequences are specified, not by applying information theory, but my making experimental assessments of the function of those sequences within the overall apparatus of gene expression. The same application of specificity would be applied to complexity. Given the complexity of the components need for and to sustain life, the complexity is that which maintains function, a specified complexity.
When arguing for design, the argument cannot take one to Christianity or even God. All one can purport is an intelligent cause. The evidence cannot identify who or what the cause is. This is constructive empiricism. Constructive empiricism states that one can only refer to the aspects of that being, in this case, the intelligence of the cause, respective to the issue and evidence at hand. It is only be a cumulative case argument can one infer that the intelligent cause is God.
By experience, it can be deduced that mind originates information (as previously described) and that the other competing hypotheses do not have the explanatory scope and power as design does. It is by the means of abduction one can infer that design, or intelligent causation, is the best explanation for the data. Chance and randomness cannot substantially account for the data. The improbability alone is infinitesimally improbable. The necessity explanation has no support and the physical variations of the cosmic landscape place the explanation at implausible.
 William A. Dembski, The Design Inference (Cambridge: Cambridge University Press, 1998).
 This equated the amount of information transmitted with the amount of uncertainty reduced or eliminated by a series of symbols or characters. Claude Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal 27 (1948): 379-423; 623-656.
 Claude Shannon, W. Weaver, The Mathematical Theory of Communication (Champaign, IL: University of Illinois Press, 1998), 8.
 Stephen C. Meyer, “ A Scientific History—and Philosophical Defense—of the Theory of Intelligent Design.”
 Ibid. To avoid equivocation, it is necessary to distinguish “information content” from mere “information carrying capacity,” “specified information” from mere “Shannon information,” “specified complexity” form mere “complexity.”
 Intelligent causation is entirely consistent with the scientific method. For example: The design inference begins with the observation that intelligent agents produce complex specified information. The hypothesis would follow with predictions of design. For experiments, one would one need to test whether scientific data has complex specified information. The conclusion may follow as: Because X exhibits high levels of complex specified information, a quality known to be a product of intelligence, therefore, life was designed.