Page 189 - Statistics for Environmental Engineers

L1592_frame_C22 Page 189 Tuesday, December 18, 2001 2:43 PM

TABLE 22.2
Attributes of a Good Experiment
A good experimental design should:

1. Adhere to the basic principles of randomization, replication, and blocking.
2. Be simple:
a. Require a minimum number of experimental points
b. Require a minimum number of predictor variable levels
c. Provide data patterns that allow visual interpretation
d. Ensure simplicity of calculation
3. Be flexible:
a. Allow experiments to be performed in blocks
b. Allow designs of increasing order to be built up sequentially
4. Be robust:
a. Behave well when errors occur in the settings of the x’s
b. Be insensitive to wild observations
c. Be tolerant to violation of the usual normal theory assumptions
5. Provide checks on goodness of fit of model:
a. Produce balanced information over the experimental region
b. Ensure that the fitted value will be as close as possible to the true value
c. Provide an internal estimate of the random experimental error
d. Provide a check on the assumption of constant variance

Blocks of Time Randomized Blocks of Time

time time
A A A B B B C C C C A B A C B B A C

(a) Good and bad designs for comparing treatments A, B, and C

A A A A B B B B C C C C D D D D
No blocking, no randomization

ABCD BCDA CDAB DABC
Blocking and Randomization
(b) Good and bad designs for comparing treatments A, B, C,
and D for pollution reduction in automobiles

A A A C A B
B B B B C A
C C C A B C

(b) Good and bad designs for comparing treatments A, B, and
C in a field of non-uniform soil type.

FIGURE 22.2 Successful strategies for blocking and randomization in three experimental situations.
© 2002 By CRC Press LLC