Finding patterns isn't really a question about random processes; it's a question about the human brain.
-Mike Loukides
Excerpts;
Random events are inherently clumpy. It's tempting to think that small numbers of random events should be "evenly spaced" (whatever that might mean). That's not the case. If events weren't clumpy, the events wouldn't be random. Tossing 10 consecutive heads sounds unlikely. But if you flip a coin a million times, 10 consecutive heads will show up several hundred times. Clumps of 20 consecutive heads are much rarer, but there's a very good chance they will show up once or twice in a million tosses. Look just at those consecutive heads, and you've got a nice "clump." (You can compute the probabilities fairly easily using the Binomial Distribution, but it's more fun to write a program.)
What would be really surprising, though, would be to find a "non-clumpy" distribution in which heads and tails are perfectly evenly distributed: HTHTHTHTHTHTHTHTHTHTH.... If we saw that sequence, from the first toss to the last, we'd be justified in thinking that something was wrong. Of course, the probability of getting 10 or 20 or 200 alternating heads and tails (starting with heads) is non-zero; for that matter, it's exactly the same as the probability of 10, 20, or 200 consecutive heads.
Looking at any set of data in retrospect, it's easy to find all kinds of patterns. Seeing patterns in data that aren't really there is a mistake that's all too common among data scientists.