Going through the Profiles in Research published by the Journal of Educational and Behavioral Statistics, I was amused to see the following concluding paragraph in the interview with Lyle Jones:

Despite my [Jones’s] strong preference for interval estimation, there are situations for which a test of significance still may be appropriate. One is multiple comparisons, such as comparisons between all pairs of states for average student achievement scale scores in NAEP [National Assessment of Educational Progress]. A related application is assessing the goodness of fit of a model to an array of values. In these cases, interval estimation is not easily employed and the careful application of significance tests may continue to serve about as well as any alternative.

No! Not at all! My paper with Jennifer and Masanao specifically shows how interval estimation (i.e., multilevel modeling) solves the NAEP comparisons problem just fine (setting aside the question of whether we should be interested in these state-level averages in the first place). It’s good to knows that some progress has been made since 2003.