Location: Fruit and Tree Nut ResearchTitle: How rater bias and assessment method used to estimate disease severity affects hypothesis testing in different experimental designs
|CHIANG, KUO-SZU - Chung Hsing University|
|LEE, I-HSUAN - Chung Hsing University|
|EL JARROUDI, MOUSSA - University Of Liege|
|DELFOSSE, PHILIPPE - Centre De Recherche Public - Gabriel Lippmann|
Submitted to: Phytopathology
Publication Type: Abstract Only
Publication Acceptance Date: 5/8/2015
Publication Date: 11/1/2015
Citation: Chiang, K., Bock, C.H., Lee, I., El Jarroudi, M., Delfosse, P. 2015. How rater bias and assessment method used to estimate disease severity affects hypothesis testing in different experimental designs [abstract]. Phytopathology. 105:S4.25.
Interpretive Summary: Abstract only.
Technical Abstract: The impact of rater bias and assessment method on hypothesis testing was studied for different experimental designs for plant disease assessment using balanced and unbalanced data sets. Data sets with the same number of replicate estimates for each of two treatments are termed ‘balanced’, and those with unequal numbers of replicate estimates are termed ‘unbalanced’. The three assessment methods considered were nearest percent estimates (NPEs), an amended 10% incremental scale, and the Horsfall-Barratt (H-B) scale. Estimates of severity of Septoria leaf blotch (SLB) on leaves of winter wheat were used to develop distributions for a simulation model. The experimental designs are presented here in the context of simulation experiments which consider the optimal design for the number of specimens (individual units sampled) and the number of replicate estimates per specimen for a fixed total number of observations (total sample size for the treatments being compared). The criterion used to gauge each method was the power of the hypothesis test. As expected, at a given fixed number of observations, the balanced experimental designs invariably resulted in a higher power compared with the unbalanced designs at different disease severity means, mean differences, and variances. The number of replicate estimates taken per specimen can be small – 2 replicate estimates are recommended. Furthermore, for biased estimates, a difference in the power of the hypothesis test was observed between different assessment methods and between different experimental designs. Results indicated that, regardless of experimental design or rater bias, the H-B scale is more likely to cause a Type II error. These results suggests that choice of assessment method, optimizing sample number and replicate estimate and using a balanced experimental design are important criteria to consider to maximize the power of hypothesis tests for comparing treatments using disease severity estimates.