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Doesn't the addition of this corroborating evidence oblige us to raise our probability assessment for the subject proposition?
It is generally deemed reasonable to answer this question "yes," and for a good many this "yes" is not only reasonable but incontrovertible.
This is a combination of a generalization and a statistical syllogism, where the conclusion of the generalization is also the first premise of the statistical syllogism.
The basic form of inductive inference, simply induction, reasons from particular instances to all instances, and is thus an unrestricted generalization.
How much the premises support the conclusion depends upon (a) the number in the sample group, (b) the number in the population, and (c) the degree to which the sample represents the population (which may be achieved by taking a random sample).
The hasty generalization and the biased sample are generalization fallacies. “Six of the ten people in my book club are Libertarians.
A generalization (more accurately, an inductive generalization) proceeds from a premise about a sample to a conclusion about the population.
For suppose we do discover some new organism—let's say some microorganism floating in the mesosphere, or better yet, on some asteroid—and it is cellular.So then just how much should this new data change our probability assessment?Here, consensus melts away, and in its place arises a question about whether we can talk of probability coherently at all without numerical quantification. It truncates "all" to a mere single instance and, by making a far weaker claim, considerably strengthens the probability of its conclusion.Two dicto simpliciter fallacies can occur in statistical syllogisms: "accident" and "converse accident".Simple induction proceeds from a premise about a sample group to a conclusion about another individual.