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NLP was developed in the mid-70s by John Grinder, a Professor at UC Santa Cruz and Richard Bandler, a graduate student. NLP, as most people use the term today, is a set of models of how communication impacts and is impacted by subjective experience. It's more a collection of tools than any overarching theory.

Much of early NLP was based on the work of Virginia Satir, a family therapist; Fritz Perls, founder of Gestalt therapy; Gregory Bateson, anthropologist; and Milton Erickson, hypnotist.

by Harry Southworth

Date: Wed, 13 Dec 1995 15:57:08 GMT
From: sta5rs@amsta.leeds.ac.uk (Harry Southworth)

I have just skimmed through an article by J.Cohen (Cohen, J. (1994). The earth is round (p<.05). American Psychologist, 49, 997-1003.) As far as I could tell, it seemed to be saying that people who don't understand statistical models should not be using them. Even if the model is understood, it is extremely important to be sensible when using it. It has also been brought to my attention that there are moves in the making for phasing out statistical significance testing in the field of psychology.

What follows are a few comments and opinions of MY OWN! This is not intended to be taken as law. (I am a final year PhD student in statistics, and I have a BSc in economics and statistics, and an MSc in probability and statistics.)

I read on a Stever Robbins' WWW page about some potential pitfalls in trying to 'prove' NLP. It was mentioned there that non-NLP therapists are bound to use NLP techniques and that this might cause problems in hypothesis testing. Actually, I don't think that this is true, in itself; such factors can be built into a statistical model.

Nevertheless, there are indeed problems in applying statistical models to testing hypotheses in psychology. People are not just simple machines that follow rules and can be modelled by a nice equation and will vary around that equation with a nice normally distributed residual. The number of factors that affect the way that an individual will behave is enormous and is far too complex to be packaged up as 'residual error'.

A trivial example might be 'A cup of coffee with x amount of caffeine will cause a y-minute long improvement in concentration, followed by a z-minute long reduction'. This assertion will only apply to certain individuals in certain conditions. I will certainly not be true for all people at all times. For example, the amount of sleep that a person has had recently, the proximity of his deadlines, the temperature in the room, how comfortable his clothing is, the alcohol level of his blood, the last time he excercised, blahdeblah blah, might affect the process to one degree or another. It is these factors, not specified in the model, that are taken up by the residual.

The way in which an individual's behavious will vary around the predicted level will be extreme. The conditions of the lab in which the original experiment was performed will not be replicated. Even if the same individuals are tested again, with the conditions in the lab as close as possible to being replicated, unless each individual has had no sensory experience at all, either internal or external (i.e. he is in a coma and his brain has liquefied) then some of the unspecified factors which constitute the residual in the model will have changed: that is, the original model will not be valid. The best that we can say is that 'A cup of coffee with x amount of caffeine in it might have some affect on some people some of the time in some situations'.

William James (1842-1910) said, "We must be careful not to confuse data with the abstractions we use to analyse them". This is a bit like saying, "We must remember that our statistical models are maps, not territories."

Besides all of which, what is it that needs proving? Cohen gives an example in which a researcher's hypothesis is that a populations is completely free of a certain disease. He observes an individual within the population who carries the disease, and then starts to calculate confidence intervals to decide whether or not his hypothesis is correct. A similar example involving 'proving' NLP can be drawn:

It has often happened that I have been trying to explain the concept of submodalities to a friend who has been insisting that they don't matter a damn. If I then ask that person to close his eyes and recall a time in the recent past when he enjoyed particuarly good sex... just one particular time... (point out that I don't want to know any content, thank you very much)... ask him to notice any images that he might be noticing in his head... have you got that...?... so now take that picture and make it bigger-and-brighter-and-closer-and...

I don't usually get any further than this. The person in question tends to jolt backwards and open his eyes with a particularly entertaining expression on his face. He will usually concede that submodalities can affect the way he feels, although he will often deny that it is possible to manipulate submodalities deliberately (despite the fact that he's just done it).

So, (assuming that my friend and I aren't fibbing), given that I know that submodalities affect the way that I feel, and that they affect the way that my friend feels, I've proved that at least one aspect of NLP can affect some people in some way, sometimes in some situations, and I don't need a p-value or a confidence interval to back me up.

(Since NLP is nothing but lies, all of the above is of no interest.)

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