Hypothesis, Theory, Model and Law
When thinking about ideas in a scientific context the ideas in question get described according to the level of corroboration and scrutiny they have recieved. In scientific disciplines, the words, “hypothesis”, “theory”, “model” and “law” hold different connotations in relation to the stage of acceptance or knowledge about a group of phenomena or ideas.
The first step in the scientific process is to propose a solution or answer to the problem or question. In science, this suggested solution or answer is called the hypothesis, and this is one of the most important steps a scientist performs. A scientific hypothesis is an informed, testable, and predictive solution to a scientific problem that explains a natural phenomenon, process, or event.
The term derives from the ancient Greek, hypotithenai meaning “to put under” or “to suppose”. Scientific method requires that one can test a hypothesis. In early usage, scholars often used the word hypothesis to refer to a clever idea or to a convenient mathematical approach which simplified cumbersome calculations.
In common usage, a hypothesis refers to a provisional idea whose merit requires evaluation. For proper evaluation, the one who frames a hypothesis needs to define the specifics in operational terms. A hypothesis indicates that more work needs to be done by the researcher in order to either confirm or disprove it. In due course, a confirmed hypothesis may become part of a theory or may become a theory itself.
Any useful hypothesis will enable predictions by reasoning. It might predict the outcome of an experiment in a laboratory setting or the observation of a phenomenon in nature. The prediction may also invoke statistics and only talk about probabilities.
Karl Popper, among others, has argued that a hypothesis must be falsifiable, and that a proposition or theory cannot be called scientific if it does not admit the possibility of being shown false. By this criterion, it must at least in principle be possible to make an observation that would disprove the proposition as being false, even if one has not yet made that observation.
Here is one of his works: http://strangebeautiful.com/other-texts/popper-logic-scientific-discovery.pdf
A falsifiable observation can greatly simplify the process of testing to determine whether the hypothesis has instances in which it is false. To make it a hypothesis it is essential that the outcome be currently unknown or reasonably under continuing investigation. Only in this case does the experiment, test or study potentially increase the probability of showing the truth of an hypothesis.
A proposition may take the form of asserting a causal relationship. An example of a proposition often involves an assertion of causation: If a particular independent variable is changed there is also a change in a certain dependent variable. This is referred to as an “If and Then” statement whether or not it asserts a direct cause-and-effect relationship. An hypothesis about possible correlation does not stipulate the cause and effect per se, only stating that ‘A is related to B’.
Causal relationships can be more difficult to verify than correlations, because variables are often also involved which may give rise to the appearance of a direct cause-and-effect relationship, but which upon further investigation turn out to be more directly caused by some other factor not mentioned in the proposition. An observation of a change in one variable, when correlated with a change in another variable, can actually mistake the effect for the cause, and vice-versa (i.e., potentially get the hypothesized cause and effect backwards).
Empirical hypotheses that experimenters have repeatedly verified may become sufficiently dependable that, at some point in time, they become considered as ‘proven’. Applied skepticism is an important aspect in the evaluation of hypothesis. In a strict sense, scientists should never claim that a hypothesis is “proved” in an absolute sense because proof is something found only in mathematics and logic; disciplines in which all logical parameters or constraints can be defined, and something that cannot said to be true in the natural world.
In this context, the use of the word “corroborated” rather than “proved” is preferable, but the meaning is essentially the same. Another way to refer to such repeatedly verified hypotheses would be to simply refer to them as “adequately verified” or “dependable”. In statistics the concept of a hypothesis is more general, and involves making assertions about the probability distributions or likelihoods of events.
To give a simple example, there might be two hypotheses about a coin; hypothesis A that it is “fair” (equally likely to yield heads or tails) and hypothesis B that it was biased to give a 90% probability of heads. No finite sequence of results could utterly falsify either hypothesis. However various statistical approaches (such as Bayesian statistics and t-tests) can be used to quantify the strong intuition that hypothesis A is less likely than hypothesis B.
More complex science experiments are generally evaluated statistically rather than as simple verification of falsification. If confirmed, the hypothesis is not necessarily proven, but instead remains provisional. Thus an hypothesis is a limited statement regarding cause and effect in specific situations; it may also refer to our state of knowledge before experimental work has been performed and perhaps even before new phenomena have been predicted.
Albert Einstein said “A hypothesis is a statement whose truth is temporarily assumed, whose meaning is beyond all doubt. The supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience”
Commonly hypotheses hold the form of a mathematical model. Sometimes, they can also be formulated as existential statements, stating that some particular instance of the phenomenon being studied has some characteristic and causal explanations, which have the general form of universal statements, stating that every instance of the phenomenon has a particular characteristic.
Elements of a Hypothesis
Hypothesis refers to a logical but unproven explanation for a given set of facts used as a starting point for further experimentation and observation. A hypothesis must be testable, or it is a worthless hypothesis. A hypothesis is tested by comparing results of experiments with the hypothesis’ predictions.
- Simplicity – discouraging the postulation of excessive numbers of entities
- Scope – the apparent application of the hypothesis to multiple cases of phenomena
- Fruitfulness – the prospect that a hypothesis may explain further phenomena in the future
- Conservatism – the degree of “fit” with existing recognized knowledge-systems
A hypothesis is a suggested explanation of a phenomenon, or alternately a reasoned proposal suggesting a possible correlation between or among a set of phenomena. If the experiments bear out the hypothesis it may come to be regarded as a theory or law of nature. If the experiments do not bear out the hypothesis, it must be rejected or modified.
William Glen states that the success of a hypothesis, or its service to science, lies not simply in its perceived “truth”, or power to displace, subsume or reduce a predecessor idea, but perhaps more in its ability to stimulate the research that will illuminate bald suppositions and areas of vagueness.
A theory is a hypothesis that has been tested numerous times and found to explain previous observations and make accurate predictions about future observations. The division between hypothesis and theory is a bit fuzzy, but is broadly when there is no more ‘reasonable doubt’ of the hypothesis’ truth. Broadly speaking ‘enough’ different people have tested the theory experimentally in ‘enough’ different ways that we can be reasonably sure that the theory is correct.
The defining characteristic of a scientific theory is that it makes testable (falsifiable) predictions about things not yet observed. The relevance, and specificity of those predictions determine how potentially useful the theory is. A would be theory that makes no predictions which can be observed is not a useful theory. Predictions which are not sufficiently specific to be tested are similarly not useful. In both cases, the term ‘theory’ is inapplicable.
A theory is a logically self consistent framework for describing the behavior of a related set of phenomena. It originates from, and/or is supported by, experimental evidence obtained through application of the scientific method. In this sense, a theory is a systematic and formalized expression of all previous observations that is predictive, logical and testable.
In principle, scientific theories are always tentative, and subject to corrections or inclusion in a yet wider theory. Commonly, a large number of specific hypotheses may be logically bound together by just one or two theories. The term theoretical is sometimes used to describe a result that is predicted by theory but has not yet been adequately tested by observation or experiment. It is not uncommon for a theory to produce predictions that are only later confirmed by experiment.
Humans construct theories in order to explain, predict and master phenomena (e.g. inanimate things, events, or the behaviour of animals). In many instances we are constructing models of reality. A theory makes generalizations about observations and consists of an interrelated and coherent set of ideas.
The use of assumptions is sometimes employed in formulating a theory. An example may be seen in how Albert Einstein put forth his Special Theory of Relativity. He took two phenomena which had been observed – that the “addition of velocities” is valid (Galilean transformation), and that light did not appear to have an “addition of velocities” (Michelson-Morley experiment).
He assumed both observations to be correct, and formulated his theory, based on these assumptions, by simply altering the Galilean transformation to accommodate the lack of addition of velocities with regard to the speed of light. The model created in his theory is, therefore, based on the assumption that light maintains a constant velocity (or more precisely; the speed of light is a constant).
Isaac Asimov said that an assumption is something accepted without proof, and it is incorrect to speak of an assumption as either true or false, since there is no way of proving it to be either – if there were, it would no longer be an assumption. It is better to consider assumptions as either useful or useless, depending on whether deductions made from them corresponded to reality…. On the other hand assumptions are the weak points in any argument, as they have to be accepted on faith within a philosophy of science which prides itself on its rationalism. Since we must start somewhere, we must have assumptions, but at least let us have as few assumptions as possible.
Here is some of his writing: http://www.palisadessd.org/cms/lib03/pa01000106/centricity/domain/232/asimovwrong.pdf
Theories are not easily discarded; new discoveries are generally first assumed to fit into the existing theoretical framework. Theory is generally consistent with pre-existing theory to the extent that the pre-existing theory was experimentally verified, though it will often show pre-existing theory to be wrong in an exact sense.
It is only when, after repeated experimental tests, a new phenomenon cannot be accommodated that the scientific community seriously questions the theory and attempts to modify it. General models and theories, according to philosopher Stephen Pepper (1942), are predicated on a “root” metaphor which constrains how scientists theorize and model a phenomenon and thus arrive at a testable hypotheses.
More on Stephen Pepper’s thinking here: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1338844/pdf/jeabehav00033-0099.pdf
A Theory is:
A theory is a relatively corroborated explanation of a phenomenon or reasoned proposal suggesting a correlation between multiple phenomenon. Supported by many multiple of evidence rather than a single foundation, ensuring that it is probably a good approximation, if not totally correct – tentative, correctable and dynamic, in allowing for changes to be made as new data is discovered, rather than asserting certainty. The most parsimonious explanation, sparing in proposed entities or explanations.
Central criterion of the scientific status of a theory is its falsifiability, refutability, or testability. In the development of new theory it often is not an event that discounts prior models of theory.
Usually novel theory is simply an expansion, refinement or a rearrangement of established theory. Often these refinements, expansions and rearrangements of established theory reinforce our confidence in the pre-existing tenets already laid out. New theories sometimes arise upon the realization that certain terms had not previously been sufficiently clearly defined.
For example, Albert Einstein’s first paper on relativity begins by defining simultaneity and the means for determining length: https://www.marxists.org/reference/archive/einstein/works/1910s/relative/relativity.pdf
Theories do not have to be perfectly accurate to be scientifically useful. The predictions made by classical mechanics are known to be inaccurate, but they are sufficiently good approximations in most circumstances that they are still very useful and widely used in place of more accurate but mathematically difficult theories. Sometimes it happens that two theories are found to make exactly the same predictions. In this case, they are indistinguishable, and the choice between them reduces to which is the more convenient.
The word model is reserved for situations when it is known that a hypothesis has, at least, limited validity. An often cited example of this is the model of the atom, in which, in an analogy to the solar system, the electrons are described as moving in circular orbits around the nucleus.
This is not an accurate depiction of what an atom “looks like”. The model succeeds in mathematically representing the energies but it does not represent other aspects of the quantum states of the electron. Several commentators have stated that the important difference between theories and models is that the first is explanatory as well as descriptive, while the second is only descriptive (although still predictive in a more limited sense).
Another example of how theories are models may be seen from theories on the planetary system. The Greeks formulated theories that were recorded by the astronomer Ptolemy. In Ptolemy’s planetary model, the earth was at the centre, the planets and the sun made circular orbits around the earth, and the stars were on a sphere outside of the orbits of the planet and the earth.
Retrograde motion of the planets was explained by smaller circular orbits of the individual planets. This could be illustrated as a model, and could even be built into a literal model. Mathematical calculations could be made that predicted, to a great degree of accuracy, where the planets would be. His model of the planetary system survived for over 1500 years until the time of Copernicus.
One can see that a theory is a model of reality, which explains certain scientific facts; yet the theory may not be a true or definitively accurate picture of reality. Another more accurate model can later replace the previous model. An expression which obviates the difference found between a model and reality is ‘the map is not the territory’, or ‘the menu is not the food’.
Alfred Korzybski wrote about non aristotelian ways of valuing here: http://esgs.free.fr/uk/art/sands-sup3.pdf
Central to the nature of models, from general models to scale models, is the employment of representation (literally, “re-presentation”) to describe particular aspects of a phenomenon or the interaction among a set of phenomena. In example, a scale model of a house or of a solar system is clearly not an actual house or an actual solar system. The aspects of an actual house or an actual solar system represented in a scale model are, only in certain limited ways, representative of the actual entity. In most ways that matter, the scale model of a house is not a house.
A highly corroborated hypothesis can become something else in addition to reliable knowledge – in language it may become regarded as a scientific law. A scientific law is a highly corroborated hypothesis that has been so repeatedly tested and for which so much reliable evidence exists, that it would be irrational to deny it.
This type of reliable knowledge is the closest that scientists can come to the ‘truth’ about the universe. Theories in physics are often formulated in terms of a few concepts and equations, which are identified with “laws of nature”, suggesting their universal applicability.
Scientific laws are similar to scientific theories in that they are principles which can be used to predict the behaviour of the natural world. Both scientific laws and scientific theories are typically well supported by observations and/or experimental evidence.
Usually scientific laws refer to rules for how nature will behave under certain conditions. Scientific theories are often more overarching explanations of how nature works and why it exhibits certain characteristics. Accepted scientific laws become part of our understanding of ourselves in the universe and the basis for exploring less well understood areas of knowledge.