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Find definitions for the terminology used throughout the Understanding Science pages.

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human subjects protocols
Ethical guidelines for research involving human participants. To learn more, visit Scientific culture: Great expectations.

A proposed explanation for a fairly narrow set of phenomena, usually based on prior experience, scientific background knowledge, preliminary observations, and logic. To learn more, visit Science at multiple levels.

inductive reasoning
Making a generalization based on many individual observations.

To figure out through logical reasoning. Inferences are often based on established knowledge and/or assumptions.

In science, the term law usually refers to a generalization about data and is a compact way of describing what we'd expect to happen in a particular situation. Scientific laws may have exceptions, and like other scientific knowledge, may be modified or rejected based on new evidence and perspectives. To learn more, visit our misconception on laws.

line of evidence
Evidence drawn from one sort of test result that bears on the accuracy of an idea. In science, it is often desirable to use multiple lines of evidence (drawn from different sorts of tests and even different fields of study) to evaluate a scientific idea.

In science, the term model can mean several different things (e.g., an idea about how something works or a physical model of a system that can be used for testing or demonstrative purposes). However, as a research method, modeling often means creating a mathematical model — a set of equations that indirectly represents a real system. These equations are based on relevant information about the system and on sets of hypotheses about how the system works. Given a set of parameters, a model can generate expectations about how the system will behave in a particular situation. A model and the hypotheses it is based upon are supported when the model generates expectations that match the behavior of its real-world counterpart. Modeling often involves idealizing the system in some way — leaving some aspects of the real system out of the model in order to isolate particular factors or to make the model easier to work with computationally.

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