There is plenty of research into the so-called "random SAT" problem, where we basically try to solve SAT instances with clauses chosen "at random" in some sense.
There are all kinds of variations of this problem, mostly referring to how the clauses are chosen. Typically you see it written as the random "$k$-SAT" problem, where the clauses have $k$ variables, and where we can also vary the total number of variables and the total number of clauses we will have. We can also look at different probability spaces the clauses are drawn from (is it uniformly at random or something else?).
My big-picture question is, in general, how computationally difficult it is to solve these "random" instances, given some common choices of these parameters. I would be happy to know even just for uniformly at random 3-SAT w/ $n$ variables and $m$ clauses. I have read that many "real-world" industrial SAT instances are much harder to solve than these "random" ones, indicating that perhaps the random-SAT problem is easier than the general SAT problem. There also seem to be various algorithms to solve random SAT instances (although I don't know how fast or successful they are). Also, for random 3-SAT w/ uniformly drawn clauses, it is known that the ratio of clauses to variables is important; if this ratio is less than ~4.25, instances are usually satisfiable, and if the ratio is greater than ~4.25, they are usually unsatisfiable, sometimes called the "threshold of unsatisfiability" for 3-SAT. There are similar analyses for other random k-SATs. This must mean that there is some way to determine if such random instances are satisfiable to begin with.
So my question: basically, at a high level, does this make "random k-SAT" somehow easier to solve than general k-SAT? Does this mean that it is in a different complexity class somehow and if so, what would it be?