Nature and Philosophy of Science

"A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it." - Max Planck

The goal of Science is to obtain True Knowledge of Reality, precisely the same goal as that of the Ancient Gnostics.

Philosophy of science deals with the systemic nature of scientific inquiry which is equivalent to gnostic inquiry.

To properly understand the philosophy of science, which is also to understand the methods of gnostic inquiry, it is necessary to understand basic components of science which include data, theories, and shaping principles.

Collections of information about physical processes are termed data.

Collecting data to support scientific theories is laborious.

Fundamental assumptions are made in data collection.

Details of that process are often excluded when forming a scientific theory.

Vague or overgeneralized data is easier to fit into a scientific theory than specific data.

Scientific theories come in two forms.

Phenomenological theories are empirical generalizations of data.

They merely describe the recurring processes of nature and do not refer to their causes or mechanisms.

Phenomenological theories are also called scientific laws, physical laws, and natural laws.

Explanatory theories attempt to explain the observations rather than generalize them.

Whereas laws are descriptions of empirical regularities, explanatory theories are conceptual constructions to explain why the phenomena exist.

Shaping principles are non-empirical fundamental assumptions that form the basis of science and go into selecting every theory.

Originally science, a systematic way of acquiring knowledge, was seen as absolutely objective, rational, and based on purely empirical observations.

This traditional image of science held that scientific theories and laws were to be conclusively confirmed or conclusively falsified based on objective data.

It was believed that "the scientific method" excluded cognitive biases, emotion, intuition, assumptions and was based entirely on logic and reason.

The definition of what "the scientific method" is has changed over time.

In the early seventeenth century Baconian inductivism was considered to be "the scientific method."

The basic concept: collect as numerous of observations as humanly possible, remain unaffected by any prior prejudice, theoretical preconceptions or cognitive bias while gathering the data, inductively infer theories from those data (by generalizing the data into physical laws), and collect more data to modify or reject the hypothesis if needed.

Unfortunately, when using inductivism to arrive at natural laws, certain theoretical preconceptions are absolutely vital.

To generalize the data into physical laws, the individual must assume that the laws apply for physical processes not observed.

This results in several assumptions being held, such as a uniform operation of nature.

Even if we put aside the fact that inductive logic is invariably based on such postulations, there is another problem.

Science deals with concepts and explanatory theories that cannot be directly observed, including atomic theory and the theory of gravity.

Many other theories include unobservable concepts like forces, fields, and subatomic particles.

There is no known rigorous inductive logic that can infer those theories and concepts solely from the data they explain.

Sir Isaac Newton developed hypothetico-deductivism in the late 1600s.

Essentially, one starts with a hypothesis, basically a provisional theory, and then deduces what we would expect to find in the empirical world as a result of that hypothesis.

The concept was to quarantine human irrationality or cognitive bias.

A theory did not become a valid theory by its origins, but because of the hypothetico-deductive method of verification.

Hypothetico-deductivism fails if rigorous proof is necessary for valid science.

We must assume: sense experience, memory, and testimony are all generally reliable; we have examined all the data and there is no possibility future observations will behave unexpectedly.

Unfortuanetely every theory has an infinite number of expected empirical outcomes, and we are incapable of testing all of those expectations.

As a result of the underdetermination of theories and the risk of undiscovered, contradictory empirical evidence, a scientific theory cannot be conclusively proven merely through the data.

Karl Popper recognized that one could not record everything observed.

Some sort of selection is needed, and thus observation is always selective.

Karl Popper felt a hypothesis had to be created first for scientific investigation to begin as there is no other way to determine which data is to be observed.

Karl Popper developed the idea of falsification which suggests that if a prediction does not come true, then the scientific theory must be false.

The necessity for a scientific theory to be conclusively falsifiable is known as the demarcation criterion.

Surprisingly, the problem is that it is impossible to conclusively falsify theories by empirical data.

Scientific theories, by themselves, are incapable of making predictions.

Empirical results of an experiment invariably rest on auxiliary assumptions - assumptions auxiliary to the original fundamental assumptions.

Assume we have a particle theory that says if we process a certain particle in a particular way, we will get specified values on various measurements.

First we must assume that all foundational theories involved in deriving the prediction are correct; the models we are using in deriving the prediction are all correct; the predictions derived from those theories and specific versions of those models are all logically correct; some other things we'll skip …

Predictions of future occurence of events are dependent on the fundamental assumptions of previous scientific theories.

Unfortunately scientific theories cannot be conclusively proven.

The dependence on foundational assumptions to make predictions is sometimes called the Duhem-Quine problem.

Theories can neither be conclusively proven nor conclusively falsified by empirical data.

It is possible to salvage a troubled theory or make arguments against a well-supported theory simply by altering auxiliary assumptions to produce different predictions or change the meaning of theory-laden observations.

It is also possible to modify virtually any theory so that it's consistent with whatever data that might come up.

The shaping principles used to build theories form the foundation of science.

Examples of shaping principles include: there exists an external objective reality; uniformity of nature - the belief that natural processes operate in a fairly consistent manner; our senses are generally reliable.

Due to the underdetermination of theories, there is always an infinite number of competing theories that can accommodate any given set of empirical data.

Ockham's Razor or the Law of Parsimony, the fundamental shaping principles of logic, states that, if all other aspects are equal, the simplest theory is preferred over other theories involving additional complexity.

Science does not ain=m at establishing immutable turths and etrenal dogmas; its aim is to approach the truth by successive approximations, without claiming that at any stage final and complete accuracy has been achieved.

There are a few exceptions to the idea that there is no conclusive proof in science.

Scientists intuitively feel how rational scientific theories are, rather than having a precise logical method for making such judgements.

Most of the shaping principles are frequently unspoken and many times scientists themselves do not know they are using them.

Shaping principles influence the data we perceive as there is a tendency for the mind to unconsciously fill in patterns based on these notions.

Such human contamination is called internal theoretical orientation of data.

As a result, totally objective data can never be obtained.

A delicate tapestry, a spider's web, is woven based on fundamental assumptions and a collection of theories combined with their shaping and background principles which thus make up an explanatory matrix, or conceptual grid, in which to fit the observed data.

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