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excerpts from
Science for All Americans
by F. James Rutherford and Andrew Ahlgren
1989, 1990 by the American Association for the Advancement of Science, Inc.
page 7
Logic and close examination of evidence are necessary but not usually sufficient for the advancement of science. Knowledge and creative insight are usually required to recognize the meaning of the unexpected. The credibility of scientific theories often comes from their ability to show relationships among phenomena that previously seemed unrelated.
The essence of science is validation by observation. Theories should fit additional observations that were not used in formulating the theories in the first place.
page 8
When faced with a claim that something is true, scientists respond by asking what evidence supports it. But scientific evidence can be biased in how the data are interpreted, in the recording or reporting of the data, or even in the choice of what data to consider in the first place. Scientists' nationality, sex, ethnic origin, age, political convictions, and so on may incline them to look for or emphasize one or another kind of evidence or interpretation.
Bias attributable to the investigator, the sample, the method, or the instrument may not be completely avoidable in every instance, but scientists want to know the possible sources of bias and how bias is likely to influence evidence. Scientists want, and are expected, to be as alert to possible bias in their own work as in that of other scientists. One safeguard against undetected bias in an "area of study" is to have many different investigators or groups of investigators working in it.
It is appropriate in science, as elsewhere, to turn to knowledgeable sources of information and opinion, usually people who specialize in relevant disciplines. But esteemed authorities have been wrong many times in the history of science. In the long run, no scientist, however famous or highly placed, is empowered to decide for other scientists what is true, for none are believed by other scientists to have special access to the truth.
In the short run, new ideas that do not mesh well with mainstream ideas may encounter vigorous criticism, and scientists investigating such ideas may have difficulty obtaining support for their research. Indeed, challenges to new ideas are the legitimate business of science in building valid knowledge.
page 9
When someone comes up with a new or improved version that explains more phenomena or answers more important questions than the previous version, the new one eventually takes its place.
pages 11- 12.. . . research involving human subjects may be conducted only with the informed consent of the subjects, even if this constraint limits some kinds of potentially important research or influences the results. Informed consent entails full disclosure of the risks and benefits of the research and the right to refuse to participate.
In their work, scientists go to great lengths to avoid bias.
Page 17
Mathematics is the chief language of science. Mathematics and science have many features in common. These include a belief in understandable order; an interplay of imagination and rigorous logic; ideals of honesty and openness; the critical importance of peer criticism; the value placed on being the first to make a key discovery; and being able to use technology to open up new fields of investigation.
Page 20
But what degree of accuracy is good enough? The answer depends on how the result will be used, on the consequences of error, and on the likely cost of modeling and computing a more accurate answer.
For example, an error of one percent in calculating the amount of sugar in a cake recipe could be unimportant, whereas a similar error in computing the trajectory for a space probe (or a person's life) could be disastrous.
Page 159-160
We can be misled, as well as assisted, by metaphor or analogy. For example, the "Tree of Evolution" may incline one to think not just of branching but also of upward progress. (That would be reaching.)
The accuracy of models of complex systems is limited by a large number of interacting variables that need to be dealt with simultaneously. Or, an abstract model may fit observations very well, but have no intuitive meaning.
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