The 37% Research-Based Rule

The 37% scientific rule is a way for a person to increase the likelihood of making the right decision whether hiring the right financial analyst, choosing the right apartment, selling your house if you want to sell it or parking your car. Without this rule, one does not have a procedure to follow in order to optimize the probability of picking the right option among other existing options. With this rule, an individual settling down with the first option even if it is a great one and an individual being picky are situations that are systematically addressed under the 37% rule.

For instance, let's say that we have 11 candidates for a particular position and we would like to pick the most qualified one in the shortest amount of time because interviews clearly consume time; thus, capital. Additionally, we cannot offer a job to candidates that were previously interviewed and were not offered a job. The sequence at which you pick someone for a job interview is assumed to be random to eliminate bias. With this case, not applying the 37% rule will give us a probability of 9.09% (1/11) of hiring the most qualified person. This does not mean you cannot hire the best person if you do not follow the 37% rule, but the chances are just low.

Let's take the example of parking your car in a parking lot. If one wants to maximize the chances of parking at the right spot, then we should think of minimizing the distance from where one parks his or her car to the destination because most people do not want to park far away, especially in winter. Looking-and-leaping principle tells us when we should look and when we should leap. "Should you take this spot, or go a little closer to your destination and try your luck?" (Christian, 2016) is a question that we ask ourselves when parking our cars. In the case of the 37% rule, one should ideally switch from looking to leaping when the individual passes about 37% empty parking spaces of the total population—or all empty parking spaces in the parking lot.

Since you do not know how many empty spaces you will see, you should theoretically come up with a number of spots you think you will see based on the current environment. For the 37% rule to work, one needs to know the number of apartments, candidates or vacant parking spaces he or she thinks is maximum. Below is a chart that shows a scientific proof of 37%.

This figure was created by John Billingham for the article Kissing the frog: A mathematician's guide to mating, which looks at results and problems related to the 37% rule in more detail.

N: A rough estimate of people you could be dating/interviewing

M: The number of people you should be dating/interviewing to maximize your probability

X: Your ideal person

The chart above mathematically proves that your M, which is how many people you should see, should be around 37% of your N population in order to leverage your chances of succeeding. This means one should shift from looking to leaping when he or she sees around 37% of the people he or she is willing to see. In other words, once you see roughly 37% of the people, you should hire the first one who is better than the rest of the people you saw. Thus, the individuals that are considered 37% are your baseline and any one after that should be measured against it to determine whether to hire him or her or not.

There are some disadvantages of this rule such as you need to know the number of people or parking spots that you are willing to see as a maximum and you cannot hire someone who you interviewed and was not offered a job. However, one can return to a vacant parking space that he or she passed by making a U-turn.

In closing, I believe the advantages of this rule outweigh the disadvantages. With the 37% rule, an individual follows a system to maximize the likelihood of making the right decision. I encourage you to use this rule and compare it with how you make your day-to-day decisions. Best of luck.

Sources:

Freiberger, M. (2017) Strategic Dating: The 37% rule. Retrieved from https://plus.maths.org/content/mathematical-dating

Christian, B., Griffiths, T. (2016) Algorithms to Live By: The computer Science of Human Decision.