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The Scientific Method, Part 2

ByCrossFit March 3, 2020

We cannot move forward in our understanding of science without a thorough comprehension of the scientific method. The scientific method must be considered not as a procedure followed sequentially through numbered steps but as a set of criteria for the end product of science. It is neither a recipe nor a road map but a checklist of criteria that can be met by any route — inspiration or perspiration, methodically or haphazardly — reserving for the scientist-to-be lessons in procedural efficiency.

We have previously identified seven distinct elements of the method:

  1. Definitions
  2. Observations
  3. Measurements
  4. Models
  5. Predictions
  6. Experiments
  7. Validation

These seven elements, shown in logical order, lend themselves to a useful taxonomy, one that is compact and uses familiar terms descriptive of the whole process.

Language and Definitions 

The first logical step in the scientific method is the setting of precise terms for the discourse, or, definitions. The defining process links language, logic, and mathematics inextricably.

Before a phenomenon can be truly observed, the human mind must have the language with which to cognize and describe it. Our interactions with the world are grounded in the linguistic structure and terms by which we understand it. Researchers have found common, elementary grammatical structures in unrelated languages. Grammar appears to be hard-wired in the organization of our brains. Research might show a similar relationship one day between languages, the brain, and logic.

Arguably, all languages contain the same logic. The logical meanings assigned to semantic relationships existed in man’s languages before logicians formalized them. Perhaps we inherit mathematics as well. If the logicians are right, then mathematics is but a school of logic. Semanticists only need establish that all languages contain integers. All three subjects — languages, logic, and mathematics — might fall under the name of language. That’s confusing, however, because of the conventional, narrower sense of language as a particular tongue. Since these subjects are in a sense pre-science, we combine them here under the term foundations.

We can also note that objectivity — essential to science — begins with precision in language and its derivatives. Precision refers to the disciplines of using language with maximum clarity and extracting logic from it. It leads to abstractions and eventual mathematical representation, if possible. In one sense, these are precursors to science, and in another sense, they are defining the terms of discourse as the first step in science.

Discovery and Objectivity

Discovery and objectivity are powerful overlapping notions in the taxonomy of the scientific method. As noted above, objectivity spans both the use of a precise language and measurements used for either confirmation or validation. Discovery fits well with the scientific work of uncovering patterns in measurements, which itself is an objective pursuit.

In recent years, “discovery” has been in vogue in education as a name for the scientific process. However, when used so broadly, discovery has an unacceptable connotation. It suggests that the real world possesses not just objects but processes and laws, just waiting to be unearthed or observed. This is far too deterministic. Science is considerably more complex, revolving as it does around models that are pure constructs of the human mind. Mathematical theorems may be subject to discovery but not scientific laws in all philosophies.

We can define observations, then, as simply the registration of the real world on a sense or sensing instrument. What a human is capable of observing depends upon the training of the brain, from the stimulation of colorful objects hung over the crib to the training received as a scientist. The scientist’s task is to eliminate the subjective in these observations and to share and enlarge the observed world with others. This leads us to our next step, that of measurement.

Discovery also includes the field of measurements. Comparing with a standard is by definition measuring. Consequently, all objective observations are measurable. If a field of discourse exists containing well-defined objects, then the definitions must make these objects mutually differentiable. Discrete decisions about whether a specimen possesses some attribute will lead to an objective decision about the name of the object. Measurements in such a case involve language but may not yield to quantifying or ordering.

Measurement training also helps students develop a perspective of how brief and limited man’s observations are, and how an accuracy limitation underlies each measurement.

Creativity, Patterns, and Models

Discovery needs the counterbalance of creativity, an essential and stimulating side of science too often overlooked. Some scientific experiments are major creative feats, as great as the models they test. Finding a novel implication or prediction of a model has a good deal of creative content, for it can lead to an efficient test for validity. Creativity needs equal emphasis in the scheme, especially to offset the implications of discovery.

The term “patterns,” like discovery, is a vogue word that suits the purposes of a specific science strategy. Our preference is to preserve discovery as a major category of the scientific method that includes observations, measurements, and the extraction of patterns. Models, the core of scientific expression, are actually the expression of patterns from measurements of observations. They are thus logically included within creativity as we progress through our list of criteria.

Scientists model phenomena based on measurements. They design the models to have predictive value, reflecting the patterns they extracted from the description of nature.

Predictions, Experiments, and Validation

These scientific models require validation through confirmation of predictions of qualitatively new phenomena or relationships. Validation consists of devising experiments, gathering confirming or denying data, and subjectively evaluating the model. The predictive value of a model includes a measure of accuracy in the prediction, though not the utility of the prediction.

Science is a transformation from a domain of objective observations to a range of objective predictions.

Thus far, then, we find seven essential elements for the scientific method arising out of the defining of science. Organizing them into four categories produces a taxonomy. The table below shows the disposition of those seven elements.

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