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The Importance Of Checking The Assumptions Underlying Statistical Analysis
Most statistical techniques require that one or more assumptions be met, or, in the case that it has been proven that a technique is robust against a violation of an assumption, that the assumption is not violated too extremely. Given that the validity of any conclusion drawn from a statistical inference depends on the validity of the assumptions made, it is clearly important that those assumptions should be validated. Incorrect assumptions can generate wildly inaccurate conclusions.
The applied researcher, data scientist or data analyst who routinely adopt a traditional statistical procedure without giving thought to its associated assumptions may unwittingly produce misleading results.
Applying the statistical techniques when assumptions are not met is a serious problem when analyzing data.
Even if one desires to check whether or not an assumption is met, two problems stand in the way. First, assumptions are usually about the population, and in a sample the population is by definition not known. Second, because assumptions are usually defined in a very strict way, the assumptions cannot reasonably be expected to be satisfied. The applied researcher, data scientist or data analyst should consider what effect a departure from the assumptions might produce.
YieldWise experts can help to find a fit between assumptions of statistical technique and data characteristics. Going this way YieldWise experts can adjust statistical technique and improve data characteristics to ensure they satisfy the assumption of statistical technique.