This Simple Mathematical Formula Can Tell You When You've Met 'The One'

You'll never look at the number 37 the same way again.

If thinking about settling down with one person gives you a nasty case of FOMO, you're in luck — well, chance.

Hmmm...not quite.

Known as the "secretary problem," this statistical formula was developed to help employers have the highest probability of picking the best job candidate from a set number of options.

In a hypothetical scenario, 37 candidates are interviewing for a job. The hiring manager interviews each one, and then must immediately decide whether to hire that person or move on to the next one. Once the employer picks someone, they don't get to interview any of the remaining candidates to find out if they made the right choice.

When applied to dating, the secretary problem became known as the "marriage problem." By harnessing the power of statistics, this method can actually increase your chances of settling down with the right person.

Love’s a game of chance — but that doesn’t mean you can’t improve your probability.

The marriage problem varies slightly from the secretary problem, because in dating, your potential candidates (aka mates) can't be interviewed in rapid succession, and you can't know how many potential candidates you'll have in a lifetime. (Just ask Taylor Swift.)

To fix these issues, Peter Todd, Ph.D., of Indiana University in Bloomington, and Geoffrey Miller, Ph.D., of the University of New Mexico modified the secretary problem to estimate how many people someone needs to date before finding everlasting love.

In the original secretary problem, the best way to find the perfect candidate was to narrow down 100 applicants to a sample size of 37. To make this sample size more reasonable for daters, however, these two stats superheroes raised the number of potential candidates from 100 to 1,000 to prove only 1 to 2 percent are needed for a sample size — about 10 people.

A dater should then fill their 10-person sample size with the top 25 percent of the people within their "aspirational level," a realistic view of potential mates based on who's available and whom they can attract.

The number 37 isn't just the original sample size of the secretary problem, it's also the percentage of people you need to date and reject before picking the best candidate for love. With a sample size of 10 people, your chance of picking "the One" greatly increases after you've seen the first three to four options.

After you reach that 37 percent, follow one simple rule:

Pick the next person who is better than all your previous suitors.

The marriage problem works as a general guideline for finding love because it encourages you to settle down near the middle of your sample size. You've dated enough people to get a sense of your options without waiting too long that you've exhausted them all.

According to a 2014 study in England, the average woman will go on eleven good dates and four "disaster" dates before meeting their life mate. Conversely, the average man will go on eight dates and three blind dates.

Think of the marriage problem as counting cards for your love life. Instead of hitting 21, you're finding “the One."

While there's no sure way to win the gambling game of love, the marriage problem can help stack the deck in your favor. Commitaphobes should feel better knowing they don't need to date everyone to find the One.

That being said, the Marriage Problem does have its theoretical limitations — but that's not necessarily a bad thing.

Because real life isn’t like a mathematical formula — it’s full of uncertainty, doubts, mistakes… and, if you’re lucky, second chances.

If your first love really is the love of your life, you can still find your way back to each other — especially in the modern day of technological everything. Of course, recovering a lost relationship is harder than getting it right the first time. When you meet again though, you'll be older, wiser, and more experienced. Your renewed relationship might be all the better for it.

(H/T Washington Post)

Cover image via Unsplash

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