Trial and error is a fundamental method of problem-solving. It is characterized by repeated, varied attempts which are continued until success, or until the practicer stops trying.
According to E.L.Thorndike, learning behaviour arises when the organism faces a new and difficult situation – a problem. Trial and error is trying a method, observing if it works, and if it doesn’t, trying a new method. This process is repeated until success or a solution is reached.
This is what most of our progress and and technological advances are based upon.
Our species has encountered new problems from time to time and found a way to come out of each of those situations by learning, adapting and trying various methods in order to succeed.
Every success is based upon a hundred or even a thousand failures.
But that doesn’t mean that this is the right way of approach in every situation.
For scientists, yes, it is. Inventing and discovering something unknown requires trials and errors. But it also involves years of study, experience, practice, insights and imagination. And scientists don’t just try anything and everything.
Trial and Error was the right of approach when our species was starting to evolve. But now, with all the great minds and advances in technology, science, mathematics and every other field, we don’t have to continue solving bigger problems by using outdated methods.
There is no need for you to keep trying over and over again in order to solve a particular problem when you can rather spend that time learning, adapting and theorizing new ways and methods and calculating their probabilities and then trying the one method which appears most profitable.
For example; You can either not have any ice-creams at all in case there is no electricity anymore, or you can use Liquid Nitrogen.
Always a way out.
Trying to find a way out, or an easier solution to problems doesn’t mean you are running away. It simply means that as a species, we are all evolving.
We don’t have to continue to use those handheld fans anymore because we have evolved.
In the same way, there are practically and theoretically a million other things that you should reconsider.
Every method and theory has its pros and cons. One must learn to recognize where and when a particular approach needs to be used.
We have an in-built complex neural network. Learn to use it to its potential.