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In-Depth Information
PRESCRIPTION
Statistical methods used for experimental design and analysis should be
viewed in their rightful role as merely a part, albeit an essential part, of the
decision-making procedure.
Here is a partial prescription for the error-free application of statistics.
1. Set forth your objectives and the use you plan to make of your
research before you conduct a laboratory experiment, a clinical
trial, or survey and before you analyze an existing set of data.
2. Define the population to which you will apply the results of your
analysis.
3. List all possible sources of variation. Control them or measure
them to avoid their being confounded with relationships among
those items that are of primary interest.
4. Formulate your hypothesis and all of the associated alternatives.
(See Chapter 2.) List possible experimental findings along with the
conclusions you would draw and the actions you would take if
this or another result should prove to be the case. Do all of these
things before you complete a single data collection form and before
you turn on your computer.
5. Describe in detail how you intend to draw a representative sample
from the population. (See Chapter 3.)
6. Use estimators that are impartial, consistent, efficient, and robust
and that involve minimum loss. (See Chapter 4.) To improve re-
sults, focus on sufficient statistics, pivotal statistics, and admis-
sible statistics, and use interval estimates. (See Chapters 4 and
5.)
7. Know the assumptions that underlie the tests you use. Use those
tests that require the minimum of assumptions and are most pow-
erful against the alternatives of interest. (See Chapter 5.)
8. Incorporate in your reports the complete details of how the
sample was drawn and describe the population from which it was
drawn. If data are missing or the sampling plan was not followed,
explain why and list all differences between data that were present
in the sample and data that were missing or excluded. (See
Chapter 7.)
FUNDAMENTAL CONCEPTS
Three concepts are fundamental to the design of experiments and surveys:
variation, population, and sample.
A thorough understanding of these concepts will forestall many errors in
the collection and interpretation of data.
If there were no variation, if every observation were predictable, a
mere repetition of what had gone before, there would be no need for
statistics.
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