Visual inspection of data: does the eyeball fit the trend?

Graphs are important tools for conveying quantitative information. However, studies have shown that visual inspection of data may not be very reliable. Therefore, it is essential to identify factors that affect visual inspection. We sought to determine how subjects' accuracy in visual inspection were affected when they constructed best-fitting lines to point-to-point functions generated by lag one auto-regressive equations. Subjects were tested then retested after completing a course on experimental methodology. When asked to identify treatment effects and/or trends, subjects mean correct responses were low (36%). On retest, their correct response rates showed little change. Mean correct responses to best-fitting lines was much higher (60%), and increased on retest (71%). The disparity between correct responses for identifying treatments and/or trends and constructing best-fitting lines was in part due to parameters in the auto-regression equation. Test-retest reliability was also better for best- fitting functions than for descriptive, multiple-choice responses. Subjects continue to demonstrate difficulty in separating trends from treatment effects.