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Survey Power

By Alex Simonson

Surveys attempt to extrapolate from a sample what is happening in the defined universe as a whole. If the study is designed with biases (such as leading questions), typical statistics used for hypothesis testing are not directly relevant to determine whether there is 'statistical significance' because the statistics are unaware of such biases in the questions. Statistics are blind to the conceptual problems in questionnaire design.

Assuming, however, that we have a non-leading, non-biased instrument, and that we have a random sample of respondents, then statistics from the sample can be used to determine how likely or not these results have occurred by chance or instead are indicative of a true result. There are two general kinds of true results, results indicating the presence of some effect and that the effect truly exists in the underlying universe, and results indicating the absence of some effect and that the effect truly does not exist in the underlying universe. Thus, there are two general kinds of incorrect results, results indicating the presence of some effect, but the effect is not there in the underling universe, and results indicating the absence of some effect, but the effect is actually there in the underling universe.

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