In my last column, I discussed how even a well-designed study with a statistically significant result doesn’t necessarily mean viability in the real world. Post-study, one must study the manifestations of variation on the result in any environment in which the result is applied—and each environment will have its own unique variation.

This type of study requires the use of analytic statistical methods, which are designed to deal specifically with this type of variation by exposing it and monitoring attempts to reduce that which is inappropriate and unintended.

Not only will variation come into play in the process of applying any result, but it also affects the four statistical data processes:
1. Measurement definition
2. Data collection
3. Analysis
4. Interpretation of the analysis

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