1. Probabilistic Resources
Statistical reasoning must be capable of eliminating chance when the probability of events gets too small. If not, chance can be invoked to explain anything. Scientists rightly resist invoking the supernatural in scientific explanations for fear of committing a god-of-the-gaps fallacy (the fallacy of using God as a stop-gap for ignorance). Yet without some restriction on the use of chance, scientists are in danger of committing a logically equivalent fallacy—one we may call the “chance-of-the-gaps fallacy.” Chance, like God, can become a stop-gap for ignorance.
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High improbability by itself, however, is not enough to preclude chance. Indeed, highly improbable events happen all the time. Flip a coin a thousand times, and you’ll
participate in a highly improbable event. Indeed, just about anything that happens is highly improbable once we factor in all the ways what did happen could have happened. Mere improbability therefore fails to rule out chance.
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In addition, improbability needs to be conjoined with an independently given pattern. An arrow shot randomly at a large blank wall will be highly unlikely to land at any one place on the wall. Yet land it must, and so some highly improbable event will be realized. But now fix a target on that wall and shoot the arrow. If the arrow lands in the target and the target is sufficiently small, then chance is no longer a reasonable explanation of the arrow’s trajectory.
Highly improbable, independently patterned events are said to exhibit specified complexity.
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A probability is never small in isolation but only in relation to a set of probabilistic resources that describe the number of relevant ways an event might occur or be specified. There are thus two types of probabilistic resources, replicational and specificational. To see what is at stake, consider a wall so large that an archer cannot help but hit it. Next, let us say we learn that the archer hit some target fixed to the wall. We want to know whether the archer could reasonably have been expected to hit the target by chance. To determine this we need to know any other targets at which the archer might have been aiming. Also, we need to know how many arrows were in the archer’s quiver and might have been shot at the wall. The targets on the wall constitute the archer’s specificational resources. The arrows in the quiver constitute the archer’s replicational resources.
Note that to determine the probability of hitting some target with some arrow by chance, specificational and replicational resources multiply: Suppose the probability of hitting any given target with any one arrow has probability no more than p. Suppose further there are N such targets and M arrows in the quiver. Then the probability of hitting any one of these N targets, taken collectively, with a single arrow by chance is bounded by Np, and the probability of hitting any of these N targets with at least one of the M arrows by chance is bounded by MNp. Thus to preclude chance for a probability p means precluding chance for a probability MNp once M replicational and N specificational resources have been factored in. In practice it is enough that MNp < 1/2 or p < 1/(2MN). The rationale here is that since factoring in all relevant probabilistic resources leaves us with an event of probability less than 1/2, that event is less probable than not, and consequently we should favor the opposite event, which is more probable than not and precludes it.
Probabilistic resources comprise the relevant ways an event can occur (replicational resources) and be specified (specificational resources). The important question therefore is not What is the probability of the event in question? but rather What does its probability become after all the relevant probabilistic resources have been factored in?
Probabilities can never be considered in isolation, but must always be referred to a relevant reference class of possible replications and specifications. A seemingly improbable event can become quite probable when placed within the appropriate reference class of probabilistic resources. On the other hand, it may remain improbable even after all the relevant probabilistic resources have been factored in. If it remains improbable and if the event is also specified, then it exhibits specified complexity (complexity here being used in the sense of improbability).
2. Universal Probability Bounds
In the observable universe, probabilistic resources come in very limited supplies. Within the known physical universe there are estimated around 10^80 elementary particles. Moreover, the properties of matter are such that transitions from one physical state to another cannot occur at a rate faster than 10^45 times per second. This frequency corresponds to the Planck time, which constitutes the smallest physically meaningful unit of time. Finally, the universe itself is about a billion times younger than 10^25 seconds (assuming the universe is between ten and twenty billion years old). If we now assume that any specification of an event within the known physical universe requires at least one elementary particle to specify it and cannot be generated any faster than the Planck time, then these cosmological constraints imply that the total number of specified events throughout cosmic history cannot exceed
10^80 x 10^45 x 10^25 = 10^150.
It follows that any specified event of probability less than 1 in 10^150 will remain improbable even after all conceivable probabilistic resources from the observable universe have been factored in. A probability of 1 in 10^150 is therefore a universal probability bound. A universal probability bound is impervious to all available probabilistic resources that may be brought against it.
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Sections taken from W. Dembski’s Chance of the Gaps.