What Faults Exist in The Scientific Method?

First, I agree with much of what’s been written. This said, it would easily take several books to document all these flaws. Where to begin? (please forgive any repetitions from previous posts).

[1] Nowhere Is The Method Stated Clearly and Precisely

To begin with, the oft-promoted idea that there is a single scientific method is a complete and utter myth. Nowhere is this method stated scientifically. Imagine having to do something truly technical, but without any real instructions. This is one of the greatest flaws in the current “method.” (e.g. see Prof. Steven L. Goldman, Science Wars)

[2] The Current Method Fails to Discover Anything, Close to 100% of the Time

Truly, this is the worst flaw and the “Emperor’s New Clothes” when it comes to science and its method. Science’s primary mandate is to discover the nature of our world. Yet most scientific “discoveries” happen by accident. Even then, these accidents occur only in extremely rarely moments, especially considering the time and money we invest in scientific research.

How long would you keep a television set which turned on only once in 10,000 tries? Would you call it progress when it finally did turn on? Moreover, would you accept this process as what is necessary to discover television?

A true method would make discoveries regularly and frequently.

[3] The Method Includes No Way to Accurately Measure Real World Things

Currently, science has no way to accurately measure real world things, in part, because as Shawn infers, real world things never stand still. They constantly move and change. In lieu of having a legitimate way to deal with this, science relies on statistical measures, treating these statistics as the functional equivalent to literal fact.

Imagine wanting a child and getting statistically pregnant? How about ordering from a statistical restaurant menu? Would you just accept what you got? And what about relying on statistics to choose a spouse? This weakness underlies much of science’s method.

Ironically, in his book, The Art of Conjecture, Jacob Bernoulli, the father of statistics, warns against treating statistically derived information as fact. That’s why he calls this information, “conjecture.” So while many (but not all) of science’s claims do function adequately in the laboratory, most fail miserably when attempts are made to translate them to real world situations, especially in fields having to do with living things.

[4] The Method Includes No Standards for Defining Its Terms

What qualifies something as good ‘science?” What is science? For that matter, how do you separate good science from pseudoscience? And yes, people endlessly offer opinions as to what makes something good science. But since the days of Adelard of Bath’s 1137 treatise; Natural Questions, opinions in and of themselves have been seen as anathema to science.

Most people ignore this and see dictionary-style treatments as adequate for science. In reality though, words alone can never scientifically define anything, including its terms. In lieu of this ability, science generally relies on the language of math.

The thing is, an abstract language like math can never scientifically define real world words. For example, try defining one of the current scientific method’s favorite terms; the word, “fact.” How then do we refer to natural things other than with math?

Sadly, the current method has yet to even acknowledge this inability to properly define its terms as a problem.

[5] The Method Includes No Way to Adequately Define the Scope of Research

Another thing Shawn mentions are the shortcomings inherent in hypothesis-based science. Ostensibly, “science” is the study of nature. Yet the current method has no ability to conduct research without simultaneously ignoring the limitless natural correlations which exist within this and between any and all other research. In other words, to know the nature of anything, you must explore it in situ; in its natural setting.

You can’t know the nature of a frog if you first dissect it.

Because of the severe limits built into statistical research (e.g. it can’t account for more than two or three real world variables before failing), the current method employs reductionist approaches, guaranteeing flawed outcomes and incompleteness. This is especially true with regard to research which limits itself to artificial settings but then tries to know real world things. By doing this, the method ignores the complexity of the real world.

[6] The Method Promotes Unhealthy Competition, Rather Than Cooperation

What if all the sciences worked together? What an amazing world that would be. But respected sciences like chemistry and quantum mechanics can’t even agree on what an atom is. Forget about what happens when fighting for research money.

[7] The Method Worships Linearity in a Fractal World

Like fractals themselves, trying to explain this flaw is extremely complex. But to begin with, science uses linearity as the measure for truth. In other words, science believes that reliable, repeatable outcomes prove something is true. And in theory, this makes sense. But in the real world, only fractals can be measured properly, as nothing ever repeats the same way twice.

In truth then, fractility (which is the complementary opposite of linearity) is the only truly scientific, real world proof. And lest someone call BS on me for failing to define my terms, by Linearity (theoretical measures), I mean recognizable patterns which always repeat identically, as opposed to Fractility (real world measures), which refers to recognizable patterns which always repeat differently.

My point here is, relying on linear outcomes as the proof for truth guarantees things will not translate well to the real world. Why? Because theoretical and real world outcomes are by their very natural, different and indeed, complementary measures. And yes, both are valuable when their use includes knowing their limitations. But pretend these limitation do not exist and your method cannot and should not be claimed to be a science.

[I added this at some point]
John, yes, I agree, the current method has allowed us to make incredible advances when compared to what we were previously capable of. But this method is not only flawed. It’s preventing us from making scientific progress at the pace at which we currently live.

So no, I am not advocating for fixing the current method. In fact in my most recent book, [The Science of Discovery, 2016] , I offer an entirely new method. This method employs two new maths to make and measure discoveries: [1] logical geometry (the math of discovery) and, [2] tipping-point based math (the math of real world measurement). And while this entire book is devoted to introducing and testing this new method, even its more than 100,000 words barely scratch the surface of what this new method is capable of.

Of course the problem is, in order to grasp it, you must be able to set aside the beliefs you’ve unknowingly ingested and internalized from the previous method. This so parallels Kuhn’s description of scientific change and your equally brilliant observation that the previous method was a discovery. So while I have long since ceased apologizing for how difficult doing this can be, the new method is a discovery as well, one which has taken most of my now 70 years to make.

The point is, science is wonderful. But we can do better. Much better. And our children deserve to inherit this legacy.