The relationship between NLP and science has been a difficult one. In the eighties psychologists went out of their way to proof NLP wrong. They did this by badly testing misunderstood parts of NLP. For a large part the responsibility for this failure rests with the field of NLP as it was very unclear what NLP exactly was and who could speak on behalf of the field of NLP. This situation is hardly better today and something we want to change with ABC-NLP.
So to be very clear about it: NLP is not a science and there is no scientific proof for NLP. Yet, the reverse is also the case: there has been no correct scientific research into the right kind of NLP nor is there any scientific proof that NLP doesn’t work. In fact the term “Neuro-Linguistic Programming” has only been used in the 70s in order to hide the fact that what the first practitioners of NLP did ,was in fact hypnosis. NLP is nothing but a modern form of hypnosis. Hypnosis is currently well researched and science has clearly established that hypnosis exists, that hypnosis is safe and that hypnosis works for a number of issues. These issues happen to be the same as the issues where NLP is claimed to work which is no surprise as NLP is hypnosis.
Uninformed scientifically minded people tend to claim that NLP is pseudoscience. This is wrong. Pseudoscience is defined as:
(1) it is not scientific, and
(2) its major proponents try to create the impression that it is scientific.
NLP is not scientific so it falls under (1), but given that its major proponents wholeheartedly agree that NLP is not a science (2) fails and hence NLP has nothing to do with pseudoscience.
Instead the best way of looking scientifically at NLP is that NLP is a protoscience. NLP is the forerunner of a yet to be established scientific psychology. The most common examples of protosciences are astrology as the protoscience to astronomy and alchemy as the protoscience to chemistry. So I am sure that even the most astute critic of NLP is happy to classify NLP as a protoscience. Yet, I think NLP practitioners also have reasons to be cheerful about the idea of NLP as a protoscience because Stich makes clear in his book “Folk Psychology and Cognitive Science” (1983) that our current psychology as taught in our universities is in fact a protoscience of a yet to come scientific version of psychology. I am sure that NLP practitioners are very happy to call it a protoscience when NLP is put on par in this respect with psychology. I know I am. For an overview of the protoscientific research that we have done and the positive results that came from this research, please see: NLP protoscience.
What can NLP practitioners learn from science?
As the relationship with science has been strenuous, most NLP trainers and coaches don’t keep up with modern day science. Which is a pity because science produces a wealth of information that can be put to good use by NLP practitioners. In fact, given that NLP was born at the University of Santa Cruz, NLP has always had the notion that it should never go against well established science unless there were clear indications that science was wrong, as in the case of hypnosis in the 70s. Unfortunately, the pragmatism of NLP has let to the situation where many badly trained NLP practitioners think that anything goes in NLP. This is not the case. NLP is happy to work with the restraints that science makes highly likely. NLP practitioners who wrongly think that you can do anything you want with NLP overlook the fact that it is highly unlikely that NLP enables you to survive deep space without the right gear as science makes clear. So there is a lot that NLP practitioners can learn from science.
What’s even more important for the sound development of NLP in the future is that NLP practitioners learn from science and philosophy debating issues. Unfortunately, there are many NLP practitioners who are badly trained because their trainer himself had learned a very distorted, incomplete and incorrect version of NLP. This is not something you can blame the NLP practitioner for as it is very hard to find out which of the many training institutes the world has, actually teaches NLP correctly, completely and up to date. What is even worse is that when you point out to said badly trained and badly teaching NLP trainer that he is making mistakes you end up in a situation where he either (a) disagrees with you but also refuses to listen to you and argue for his disagreement or, what is more often the case, (b) agrees with you that he teaches NLP wrongly and then afterwards he ignores you and he keeps on teaching NLP as he always has. Although there are many things that science and philosophy can do better, it is highly unlikely that these two situations would occur within philosophy or science. If a scientist is confronted with research that what he is doing can be improved, he is very eager to check it out and learn from it. If a philosopher is confronted with unfavorable arguments that he nevertheless agrees on he cannot help himself but he has to adapt his position to take into account these new arguments. In philosophy and science you either discuss disagreements or mend your ways. It would be very healthy if NLP practitioners start doing the same.
What scientists can learn from NLP
Yet, learning is a two way street. In fact scientists can also learn a lot from NLP. This can be literally in the sense that as a form of disguised hypnosis scientists can learn from NLP how to communicate in a more hypnotic way in order to work better with other people, stress less and improve their own personal lives. More importantly, NLP is an example of how a protoscience can work from the pragmatist viewpoint. Pragmatism is very well accepted within philosophy but very few scientists have become pragmatists. NLP is a pragmatic protoscience and indicates that it is very well possible to create a pragmatic science that solves many issues.
One of the major points of criticism of uninformed critics is that NLP doesn’t care about the truth. This is correct, but misunderstood. As a pragmatic protoscience NLP is only interested in what works and refuses to think about whether something is the truth or not. Science would improve a lot once it stops looking for the truth and focuses on whether something is likely or not. A pragmatic protoscience such as NLP also needs a pragmatic form of statistics to go with its research. Fortunately, such a pragmatic statistics exists in the form of De Finetti’s subjective Bayesian statistics as described in his major work “Theory of Probability” (1974). Bayesian statistics is gaining a lot of ground recently within science and science would be better off when it at least in some parts experimented more with subjective Bayesian statistics.
In that light NLP also refuses to work with the concept of cause and effect in the grand tradition of Hume, Nietzsche and Russell. Without truth there is no cause and effect as causation implies that it is necessarily true for all people that the effect follows the cause. Instead there is only covariance. This is of course also a strong principle of science. But unfortunately while most scientists would talk this talk, they refuse to walk this walk. They might say that there is only covariance and no cause and effect, yet in their thinking, reasoning and experimentation they tend to switch back to cause and effect too easily and too confusing. Here scientists can definitely learn something from NLP.