Imagine you're sifting through resumes, deciding whom to interview. If you've put the last two candidates in the yes pile, you're much more likely to put the next resume in the no pile -- regardless of that candidate's qualifications.

Or that you day-trade index funds and the Dow has gone up the past three days. Since you figure things typically even out, you decide the market will fall today -- regardless of economic fundamentals.

Or, in simple terms, that you've flipped a coin five times and each time it's come up heads. Since that rarely happens, surely the next flip will be tails -- even though, regardless of past flips, the odds of coming up heads or tails are always 50/50.

Sound familiar? If so, you (and by you, I also mean "we") may be smarter than you think. A 2012 study determined that decisions like those are positively correlated with higher intelligence and better executive functions like working memory and conflict resolution.

So, yeah: We're smart. We understand the law of large numbers. And we can definitely spot patterns. But that doesn't mean we're right.

It's natural to assume the occurrence of a particular random event is less likely after a string of the same event. In fact, the longer the streak, the easier it is to assume that next time, the streak will break.

Your sales rep landed the last five prospects? Odds are she won't land the sixth. Your favorite team won its last eight games? Odds are they won't win the ninth. Four hires from the same recruiter turned out to be superstars? Odds are his next candidate will be a bust. 

Research shows the smarter you are, the more likely you are to think you can spot patterns. 

And then believe you can predict what will happen next.

Even when each situation is actually one-off -- and the outcome is independent of any actual pattern.

How to Avoid Gambler's Fallacy (In Spite of the Fact You're Really Smart)

In social psychology, this cognitive bias is called the representativeness heuristic, the tendency to assume a short set of random outcomes will be the same as a much longer set of outcomes. In nonscientific terms, it's called the gambler's fallacy.

Either way, the result is believing that a small sample size is representative of a much larger sample size--that since coin flips do eventually even out over the long run, that two heads in a row should mean the next flip will be tails.

Again, while "fallacy" implies "less smart," the researchers determined "the higher the cognitive ability ... the more likely" that people will engage the gambler's fallacy. 

So how can you avoid it?

First, think about whether certain events are related or independent. There is no pattern to a randomly stacked set of resumes. And one resume doesn't "know" which resume comes before or after it -- much less is able to influence the "quality" of previous or subsequent resumes.

So if two resumes in a row indicate apparent superstars, don't let that affect how you evaluate the next resume you scan. If the market has gone up the last two days, don't automatically assume it will go down today. 

The key is to think large numbers instead of small numbers. If you have determined, through actual data and analysis, that patterns do exist -- for example, that it takes 100 cold calls to land 10 prospects -- think about short-term results in that context. If two calls in a row were successful, the next call has the same likelihood of success as if the previous ten calls were failures. Over the long run, results will tend to even out.

But that evening out is a lot less likely to occur over the short-term.

So give that next call your best shot. Give every call your best shot.

Because while you might think you're smart enough to know better, you might be too smart to know better.