Below is something from the web which should provide context for the topic
for those not familiar with the concept or who might have forgotten or
misconstrued it. Occam's analogous observation, "It is vanity to do with more, that
which can be done with less", is a useful touchstone in most design.
Cheers,
Joe

Occam's Razor
one should not increase, beyond what is necessary, the number of entities
required to explain anything


Occam's razor is a logical principle attributed to the mediaeval philosopher
William of Occam (or Ockham). The principle states that one should not make
more assumptions than the minimum needed. This principle is often called the
principle of parsimony. It underlies all scientific modelling and theory
building. It admonishes us to choose from a set of otherwise equivalent models of a
given phenomenon the simplest one. In any given model, Occam's razor helps us to
"shave off" those concepts, variables or constructs that are not really
needed to explain the phenomenon. By doing that, developing the model will become
much easier, and there is less chance of introducing inconsistencies,
ambiguities and redundancies.
Though the principle may seem rather trivial, it is essential for model
building because of what is known as the "underdetermination of theories by data".
For a given set of observations or data, there is always an infinite number of
possible models explaining those same data. This is because a model normally
represents an infinite number of possible cases, of which the observed cases
are only a finite subset. The non-observed cases are inferred by postulating
general rules covering both actual and potential observations.
For example, through two data points in a diagram you can always draw a
straight line, and induce that all further observations will lie on that line.
However, you could also draw an infinite variety of the most complicated curves
passing through those same two points, and these curves would fit the empirical
data just as well. Only Occam's razor would in this case guide you in choosing
the "straight" (i.e. linear) relation as best candidate model. A similar
reasoning can be made for n data points lying in any kind of distribution.
Occam's razor is especially important for universal models such as the ones
developed in General Systems Theory, mathematics or philosophy, because there
the subject domain is of an unlimited complexity. If one starts with too
complicated foundations for a theory that potentially encompasses the universe, the
chances of getting any manageable model are very slim indeed. Moreover, the
principle is sometimes the only remaining guideline when entering domains of
such a high level of abstraction that no concrete tests or observations can
decide between rival models. In mathematical modelling of systems, the principle
can be made more concrete in the form of the principle of uncertainty
maximization: from your data, induce that model which minimizes the number of additional
assumptions.
This principle is part of epistemology, and can be motivated by the
requirement of maximal simplicity of cognitive models. However, its significance might
be extended to metaphysics if it is interpreted as saying that simpler models
are more likely to be correct than complex ones, in other words, that "nature"
prefers simplicity.

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