Exploratory Data Analysis
What If Assumptions Do Not Hold?
If some of the underlying assumptions do not hold, what can be
done about it? What corrective actions can be taken? The
positive way of approaching this is to view the testing of
underlying assumptions as a framework for learning about the
process. Assumption-testing promotes insight into important
aspects of the process that may not have surfaced otherwise.
Primary Goal is Correct and Valid Scientific Conclusions
The primary goal is to have correct, validated, and complete
scientific/engineering conclusions flowing from the analysis. This
usually includes intermediate goals such as the derivation of a
good-fitting model and the computation of realistic parameter
estimates. It should always include the ultimate goal of an
understanding and a "feel" for "what makes the process tick".
There is no more powerful catalyst for discovery than the bringing
together of an experienced/expert scientist/engineer
and a data set ripe with intriguing "anomalies" and characteristics.
Consequences of Invalid Assumptions
The following sections discuss in more detail the consequences
of invalid assumptions:
- Consequences of non-randomness
- Consequences of non-fixed location
- Consequences of non-fixed variation
- Consequences related to distributional