Experimental design matters for statistical analysis: how to handle blocking
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Experimental design matters for statistical analysis : how to handle blocking. / Jensen, Signe Marie; Schaarschmidt, Frank; Onofri, Andrea; Ritz, Christian.
I: Pest Management Science, Bind 74, Nr. 3, 2018, s. 523-534.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Experimental design matters for statistical analysis
T2 - how to handle blocking
AU - Jensen, Signe Marie
AU - Schaarschmidt, Frank
AU - Onofri, Andrea
AU - Ritz, Christian
N1 - CURIS 2018 NEXS 104
PY - 2018
Y1 - 2018
N2 - BACKGROUND: Nowadays, the evaluation of effects of pesticides often relies on experimental designs that involve multiple concentrations of the pesticide of interest or multiple pesticides at specific comparable concentrations and, possibly, secondary factors of interest. Unfortunately, the experimental design is often more or less neglected when analyzing data. Two data examples were analyzed using different modelling strategies: Firstly, in a randomized complete block design, mean heights of maize treated with a herbicide and one of several adjuvants were compared. Secondly, translocation of an insecticide applied to maize as a seed treatment was evaluated using incomplete data from an unbalanced design with several layers of hierarchical sampling. Extensive simulations were carried out to further substantiate the effects of different modelling strategies.RESULTS: It was shown that results from sub-optimal approaches (two-sample t-tests and ordinary ANOVA assuming independent observations) may be both quantitatively and qualitatively different from the results obtained using an appropriate linear mixed model. The simulations demonstrated that the different approaches may lead to differences in coverage percentages of confidence intervals and type I error rates, confirming that misleading conclusions can easily happen when an inappropriate statistical approach is chosen.CONCLUSION: To ensure that experimental data are summarized appropriately, avoiding misleading conclusions, the experimental design should duly be reflected in the choice of statistical approaches and models. We recommend that author guidelines should explicitly point out that the authors need to indicate how the statistical analysis reflects the experimental design.
AB - BACKGROUND: Nowadays, the evaluation of effects of pesticides often relies on experimental designs that involve multiple concentrations of the pesticide of interest or multiple pesticides at specific comparable concentrations and, possibly, secondary factors of interest. Unfortunately, the experimental design is often more or less neglected when analyzing data. Two data examples were analyzed using different modelling strategies: Firstly, in a randomized complete block design, mean heights of maize treated with a herbicide and one of several adjuvants were compared. Secondly, translocation of an insecticide applied to maize as a seed treatment was evaluated using incomplete data from an unbalanced design with several layers of hierarchical sampling. Extensive simulations were carried out to further substantiate the effects of different modelling strategies.RESULTS: It was shown that results from sub-optimal approaches (two-sample t-tests and ordinary ANOVA assuming independent observations) may be both quantitatively and qualitatively different from the results obtained using an appropriate linear mixed model. The simulations demonstrated that the different approaches may lead to differences in coverage percentages of confidence intervals and type I error rates, confirming that misleading conclusions can easily happen when an inappropriate statistical approach is chosen.CONCLUSION: To ensure that experimental data are summarized appropriately, avoiding misleading conclusions, the experimental design should duly be reflected in the choice of statistical approaches and models. We recommend that author guidelines should explicitly point out that the authors need to indicate how the statistical analysis reflects the experimental design.
KW - Adjuvants
KW - Analysis of variance
KW - Herbicide
KW - Linear mixed model
KW - Maize
KW - Neonicotinoid
KW - Pseudo-replication
U2 - 10.1002/ps.4773
DO - 10.1002/ps.4773
M3 - Journal article
C2 - 29064623
VL - 74
SP - 523
EP - 534
JO - Pest Management Science
JF - Pest Management Science
SN - 1526-498X
IS - 3
ER -
ID: 184876165