We’re going to talk about 3bit strings. Please note that there are 8 of them:

In particular situations we may want to concern ourselves with a subset of these strings. For example if our strings were substantially larger than 3 bits and taken to represent images, we may be interested in the subset of strings (images) that contain a face.
We can represent such subsets of strings by assigning a true/1 to strings that are in the subset, and false/0 to strings that are not.
For example,

is a way to denote the arbitrarily chosen subset of strings #{010 011 101 111}.
It is easy to see that there are 28 = 256 possible subsets of 3bit strings (since every subset is representable as an 8bit string).
Another way to represent sets of strings is by using predicate logic.
For example, the predicate logic expression (AND 0 1) represents the set of strings #{110 111} because the 0thindexed bit is true/1 and the 1stindexed bit is true/1 in both strings.
If we randomly select a logic expression from say the first 5000 such expressions in order of increasing complexity, what subset of strings are we likely to end up with? Are all of the 256 possible subsets equally likely when we are uniformly sampling logic expressions?
Here is a frequency chart demonstrating all 256 concepts and the number of logic expressions that represent them in the first 5000 predicate logic expressions:
Here is the same data in frequencyranked order (note that subsets with 0 representation are not drawn):
It seems, then, that uniformly sampling logic expressions does not yield uniformly sampled subsets of strings.
The most commonly represented subset was the empty set. (Contradictions are common, e.g. (AND 0 (NOT 0))).
The next most common subsets are representable as 0, 1, 2; in other words a single bit required to be true.
The next most common subsets are where two bits are required to be true (e.g. (AND 0 1)).
Theories on why subsets are represented nonuniformly: