Noncentrality Interval Estimation and the Evaluation of Statistical Models - Reasons Why Interval Estimates are Seldom Reported

In spite of the obvious advantages of interval estimates, they are seldom employed in published articles in many areas of science. On those infrequent occasions when interval estimates are reported, they are often not the optimal ones. There are several reasons for this status quo:

Tradition. Traditional approaches to statistics emphasize significance testing much more than interval estimation.

Pragmatism. In RS situations, interval estimates are sometimes embarrassing. When they are narrow but close to zero, they suggest that a "highly significant" result may be statistically significant but trivial. When they are wide, they betray a lack of experimental precision.

Ignorance. Many people are simply unaware of some of the very valuable interval estimation procedures that are available. For example, many textbooks on multivariate analysis never mention that it is possible to compute a confidence interval on the squared multiple correlation coefficient.

Lack of availability. Some of the most desirable interval estimation procedures are computer intensive, and have not been implemented in major statistical packages. This has made it less likely that anyone will try the procedure.