Observation

Roger Bacon (1214–1294); Galileo Galilei (1564–1624)

We start with observation — not reason. Measurement, hopefully with as much precision as possible, with a stated probable error. This is the key step. Galileo used it to reject the scholastic philosophy of St. Augustine, which had dominated by rejecting external reality in favor of introspection.

Rationalization

Multiple contributors — see sub-steps

Second comes reason: thinking about the observed data. Rationalization has five sub-steps — Assumptions, Abstraction, Selection of Descriptions, Induction, and Deduction — each with its own history and its own potential for error.

Experimentation (Controlled Observation)

Strato, 288 BC

Controlled, deliberate action to make new predicted observations under controlled conditions. This is a loop back to step A — observation — completing the cycle. The experiment tests the predictions generated by rationalization against reality.

Judgement

Socratic inquiry, 470–399 BC

From the predictions it is possible to apply value judgments to possible events — to elect those we desire and to avoid those we think we will not like. The scientific method thus allows us to select action choices which match our ethical values. However: we should NEVER use judgment to direct the formation of inductions, only observations. Using judgment to guide induction results in wishful interpretations of reality, not based on observation. This backwards step has perhaps caused more human misery than anything else.

Contradiction

Foundational principle

If there is a contradiction between what we predicted (rationalized) and what we observe, then the error was in the mental process. Nature — reality — is never wrong. The contradiction is always in our model, not in the data.

Occam's Razor — Simplest Hypothesis

William of Occam (1284–1349)

Given two hypotheses, reject the more complicated and accept the simplest. Nature operates simply; excessive complexity is usually a hint that our mental construct is partially wrong — and thus just plain wrong. "It is vain to do with more when we could do with less."

Occam's Corollary — Completeness

William of Occam (1284–1349)

Reject any hypothesis that is not complete and does not explain all the data. Parsimony without completeness is insufficient — the simplest hypothesis must also account for every observation.

Interdependence

Hippocrates ca. 460 BC; Imhotep ca. 2850 BC

The experimentalist interacts with the experiment. This must be accounted for — one must take into account one's own existence as part of the universe. The double-blind method is one way to reduce this effect, and it must not be ignored.

History

Foundational principle

The record of past events — experimental observations, data, and past rationalization (the mental constructs, hypotheses, laws, and theories) — is a foundation for science. We use past records and build upon them to avoid having to redo every single experiment for every student. Students need to redo enough experiments and deductive chains to feel confident even in those they did not personally repeat.

Anti-Authoritarianism

Francis Bacon (1561–1603); Dr. Gilbert (1540–1603); John Napier (1550–1617)

Authority may never be cited as an argument for or against any hypothesis. Theory may not be used to refute theory. Only the facts — observations — may be used to judge the success or failure of any theory. An individual who relies on authority is dealing with politics, not science. Any person who stoops to personalities and authoritarianism is not a true scientist. The scientist considers reality, data, and observations — not personality.

Reproducibility — Cause and Effect

J. S. Mill; David Hume (1711–1776)

It is assumed that experiments can be repeated, and that if the same conditions are established the same results will occur. There is a cause-effect relationship in conditions and results. Note that determination and separation of cause from effect may not be easy — an apparent cause-effect may actually both be effects from a more fundamental prior cause, or all may be co-symptoms of other unobserved causative effects.

Variability

Heraclitus (540–475 BC); W. Heisenberg (1901–1976)

Everything is in a state of change, in flux. History also consists of irreproducible results — unique situations which can never be closely approximated again. In fact all experiments are slightly different; we never exactly reproduce the same conditions in any two experiments. (Darius ca. 320 BC: "You can never step twice in the same stream.") The key to science is to get conditions close enough so that the variations do not change the outcome. This is also tied to the Heisenberg Uncertainty Principle, particularly at the microscopic level.

Irrelevance

J. H. L. Lawler (1936– ), 1969

Every variable or concept has a domain over which it is valid. Outside that domain the variables or concepts become irrelevant, and other variables or concepts must be used to describe phenomena and events. Irrelevance also steps in during the selection of descriptions to help eliminate bad choices of variables.