Random, as you no doubt are aware, means out of the blue, without rhyme or reason, hit or miss — in a word, arbitrary. Random events, by their very randomness, exhibit no discernible pattern, even if they look like a pattern; hint at a pattern; or, gosh darn it, must form a pattern if you could just figure out what the pattern is. That's precisely the part about randomness that trips us humans up: We're always looking for a pattern, and if we don't see one, we expect it to emerge eventually.
Well, randomness doesn't work that way. Random is random, and that means:
1 The absence of a pattern or the unpredictability of an outcome
1 No correlation between the outcome of one event and the outcome of another
A coin toss is an example of a random event. You toss a coin in the air (assume it's a fair coin), and it's going to come down either heads or tails. So say you toss this coin, and it lands heads. What's the chance that it'll land heads the next time you toss it? The right answer is 50 percent. But a fair number of people think (incorrectly) that it is more likely to land tails the second time because it landed heads the first time. This sort of thinking is the same kind that makes gambling so dangerous.
Imagine yourself, flush with coins, sitting at a slot machine and feeling lucky. You drop in your first coin, pull the lever, and . . . nothing happens. You put in the next coin. Again, nothing happens. And again. And again. Why do you keep playing? Probably at some level, you think that all these losses are leading up to a big win, even though you may be fully aware that a slot machine generates numbers or patterns randomly.
The important thing about the coin toss (or the slot machines) is that, as a random process, the previous outcomes don't have any bearing on the upcoming results. Just because a coin lands heads one time doesn't mean that it's less — or more — likely to land heads the next time.
Random is just plain slippery to think about so following are a couple of examples of how it can be important in biology.
Any environmental factor that affects an individual's ability to reproduce regardless of its genes can cause genetic drift. Consider a lightening strike: There's probably no genetic component that determines whether one deer or another is hit by lightning. But when a deer is hit by lightning, it won't be reproducing. Because that deer's genes won't be represented in the next generation, this random event changes the relative representation of genes in the later generation.
Essentially, these are cases of being in the wrong (or right) place at the wrong (or right) time. Examples would be a lumberjack who fells a tree on a Nobel laureate who's walking through the forest pondering his acceptance speech or the bug that crashes into your windshield. If you're not around to reproduce, or if you don't reproduce (because you prefer a neat house and travel to children, for example), natural selection can't be the driving force behind the increases or decreases in gene frequencies.
Random variations also occur in the number of offspring that different individuals have. For the minute, ignore the fact that there might be a genetic component to wanting fewer children because you prefer not stepping on those little plastic farm animals with the sharp ears. Random variation still occurs in the number of offspring that different people produce. A hundred couples all trying to have three children won't each end up with three children. On average, they'll end up with more children than couples who prefer not to have children (a matter of choice, not random factors), but they won't all end up having exactly three. That's where the random factors come in.
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