Statistical Analysis
You may have heard the saying "You can prove anything with statistics," which implies that statistical analysis cannot to be trusted, that the conclusions that can be drawn from it are so vague and ambiguous that they are meaningless. Yet the opposite is also true. Statistical analysis can be reliable and the results of statistical analysis can be trusted if the proper conditions are established.
What Is Statistical Analysis?
Statistical analysis uses inductive reasoning and the mathematical principles of probability to assess the reliability of a particular experimental test. Mathematical techniques have been devised to allow measurement of the reliability (or fallibility) of the estimate to be determined from the data (the sample, or "N") without reference to the original population. This is important because researchers typically do not have access to information about the whole population, and a sample—a subset of the population— is used.
Statistical analysis uses a sample drawn from a larger population to make inferences about the larger population. A population is a well-defined group of individuals or observations of any size having a unique quality or characteristic. Examples of populations include first-grade teachers in Texas, jewelers in New York, nurses at a hospital, high school principals, Democrats, and people who go to dentists.
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