Phenomenon – Every predictive system begins with some happening or occurrence: pinpoints of light moving across the night sky; objects that are dropped fall to the ground; smoke emanates from fire and rises into the air; living mammals are warm to the touch. 

 

Observation – In order for a phenomenon to be known, it must first be witnessed. In the case of observation, this is more than simply noticing. Observation is purposeful watching. This purpose, however, is not necessarily motivated by science or research. It could be motivated by beauty or awe, as in the case of observing a sunset or a lightning storm.

 

Data – In the broadest sense, data are collections of observations. These collections imply a certain purpose, namely, to use the observations to discern or create some kind of meaning or understanding. In a slightly narrower sense, data are recorded and then intentionally used to interpret and understand the observed phenomenon. Inherent in data collection is the assumption that data themselves have value, in that tabulation or recording of the same thing or similar things over time can reveal something about the nature of the thing itself.

 

Rule – Rules make claims about how phenomena behave. They are sometimes called laws, like Kepler’s Laws of planetary motion. These rules say that a certain phenomenon or object always happens or behaves in a particular way. Such a claim relies on data (the accumulation of recorded observations over time) in order to substantiate its universality. For phenomena that can be expected to repeat in the future, rules are inherently predictive. But, rules are not necessarily explanatory.  While rules describe the what, where, and when of phenomena, they do not necessarily address the why or how. 

 

Theory  – Theories propose a set of relationships that make claims about the cause of certain phenomena. For instance, Darwin’s Theory of Evolution by Natural Selection makes claims about the relationships between heredity, mutation, and natural selection in order to propose and describe the cause of the diverse speciation witnessed on planet Earth. While theories seek to explain a phenomenon, they do not necessarily predict its behavior.

 

Explanation – Explanations are constructed in order to answer the questions ‘Why?’ and ‘How?’ Explanations can apply to any phenomena or data, but they don’t necessarily have predictive power. This is not to say that explanations have no bearing on the future! However, while explanations make sense of past happenings and can be used to think about the future, any claims about future happenings fall into a different category.

 

Prediction  – Predictions make claims about future happenings and phenomena. While a prediction makes a claim about a future happening, it does not necessarily explain why or how that thing might or will occur. 

 

Extra thoughts on Uncertainty in predictions – There is always some degree of uncertainty inherent to the future. However, whatever uncertainty existed about a future now passed has collapsed into the unique and determined unfolding leading up to now. Students of history will be quick to point out that the interpretations of the past are anything but determined or singular, but this has to do with the amount and quality of available data as opposed to the unavailability of the happening itself! This distinction between the past and the future underlies the distinction between an explanation and a prediction. Whereas the uncertainty associated with explanations has to do with the amount and quality of its data, the uncertainty associated with predictions also includes the inherent inaccessibility of the future events themselves.

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It is important to differentiate between the common use of the word theory and a scientific theory. In lay terms, ‘theory’ is often used to express a proposed explanation about something. A scientific theory takes this much further and involves data analysis, experimental testing, and debate within the scientific community.    (Also, as Ned Hall suggested, there can be theories that give “guidelines” that are almost rules, like Darwin’s Theory of Natural Selection. See the full discussion of the Rainbow Diagram with Ned on YouTube. Discuss the idea of “heuristics.”)

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Scientists use simulations and data about the past to test theories. To do so, they might input data from, say, 1900-1950 and see how well their model predicts what actually happened in 1975. While this would seem to exemplify a prediction making claims about the past, it’s important to note that in our hypothetical situation, 1975 exists in the simulation’s future. In such scientific testing, predictions are still making claims about a future, and though it happens to be a past future from our point of view, from the perspective of the simulation, it is in the domain of the yet-to-be.

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