Statistics/Methods of Data Collection/Observational Studies

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The most primitive method of understanding the laws of nature utilizes observational studies. Basically, a researcher goes out into the world and looks for variables that are associated with one another. Notice that, unlike experiments, observational research had no Independent Variables --- nothing is manipulated by the experimenter. Rather, observations (also called correlations, after the statistical techniques used to analyze the data) have the equivalent of two Dependent Variables.

Some of the foundations of modern scientific thought are based on observational research. Charles Darwin, for example, based his explanation of evolution entirely on observations he made. Case studies, where individuals are observed and questioned to determine possible causes of problems, are a form of observational research that continues to be popular today. In fact, every time you see a physician he or she is performing observational science.

There is a problem in observational science though --- it cannot ever identify causal relationships because even though two variables are related both might be caused by a third, unseen, variable. Since the underlying laws of nature are assumed to be causal laws, observational findings are generally regarded as less compelling than experimental findings.**

Observational data are more prevalent in describing large systems like in Astronomy, Economics, or Sociology, while subjects like Physics and Psychology can be more easily subject to experiments, when they don't involve large systems, which can't be artificially constructed as easily.