Scientific method principles and applications

¶ … Scientific Method

Tyson, Neil de Grasse. (1998) "Belly Up to the Error Bar: The Scientific Method." Natural History. Retrieved on 4 Jul 2005 from Find Articles database at http://www.findarticles.com/p/articles/mi_m1134/is_n9_v107/ai

Understanding the scientific method is one of the first building blocks of teaching modern scientific research to students. But do experienced scientists concern themselves with what makes rigorous hypothesis formulation and testing? In the scientific journal of Natural History, author and scientist Neil de Grasse Tyson writes in his article "Belly Up to the Error Bar: The Scientific Method," about one of the most important parts of the scientific method in the 'real life' academic study and research of science, namely the need to minimize human bias. He calls experimental bias in favor of the initial hypothesis of the experimenter one of the greatest sources of blunders in research.

Although formal accounts of the scientific method typically describes a clinical "hypothesis-posing, experiment-conducting activity," in terms of "induction, deduction, cause, and effect," there is still a great deal of creativity and uncertainty in the formulation of said hypothesis. "Science can be a process in which practically anything goes -- from middle-of-the-night hunches to mathematical formulations driven by classical aesthetics" -- so long as the results eventually "accurately describe and predict phenomena in the real world." (Tyson, 1998, p.1)

But in terms of bias in interpreting their results, scientists are only human. "When making multiple measurements, scientists occasionally discard values that deviate strongly from their expectations." (Tyson, 1998, p.1) In the social sciences, public opinion polls are accompanied by "margins of error." (Tyson, 1998, p.1) But in science experiments as well, "some measurements will come out above the true value, while some will come out below. These are ordinary fluctuations: a chart of all the data points would look like the statistician's beloved bell curve. The history of science has shown that if an experiment is well designed, then most of the data will cluster around some value, presumably the right one." (Tyson, 1998, p.1)