Rights statement: This is the peer reviewed version of the following article: Pallmann, P. and Schaarschmidt, F. (2015), Common pitfalls when testing additivity of treatment mixtures with chi-square analyses. Journal of Applied Entomology. 140: 135–141. doi: 10.1111/jen.12258 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/jen.12258/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Common pitfalls when testing additivity of treatment mixtures with chi-square analyses
AU - Pallmann, Philip
AU - Schaarschmidt, Frank
N1 - This is the peer reviewed version of the following article: Pallmann, P. and Schaarschmidt, F. (2015), Common pitfalls when testing additivity of treatment mixtures with chi-square analyses. Journal of Applied Entomology. 140: 135–141. doi: 10.1111/jen.12258 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/jen.12258/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2016/2
Y1 - 2016/2
N2 - Studying interactions of multiple pesticides applied simultaneously in a mixture is a common task in phytopathology. Statistical methods are employed to test whether the treatment components influence each other's efficacy in a promotive or inhibitory way (synergistic or antagonistic interaction) or rather act independent of one another (additivity). The trouble is that widely used procedures based on chi-square tests are often seriously flawed, either because people apply them in a preposterous way or because the method simply does not fit the problem at hand. Browsing recent volumes of entomological journals, we found that numerous researchers have (in all likelihood unwittingly) analysed their data as if they had had a sample size of 100 or, equally bad, a sample size of one! We show how to avoid such poor practices and further argue that chi-square testing is, even if applied correctly (meaning that no technical errors are made), a limited purpose tool for assessing treatment interactions.
AB - Studying interactions of multiple pesticides applied simultaneously in a mixture is a common task in phytopathology. Statistical methods are employed to test whether the treatment components influence each other's efficacy in a promotive or inhibitory way (synergistic or antagonistic interaction) or rather act independent of one another (additivity). The trouble is that widely used procedures based on chi-square tests are often seriously flawed, either because people apply them in a preposterous way or because the method simply does not fit the problem at hand. Browsing recent volumes of entomological journals, we found that numerous researchers have (in all likelihood unwittingly) analysed their data as if they had had a sample size of 100 or, equally bad, a sample size of one! We show how to avoid such poor practices and further argue that chi-square testing is, even if applied correctly (meaning that no technical errors are made), a limited purpose tool for assessing treatment interactions.
KW - antagonism
KW - integrated pest management
KW - interaction
KW - statistical analysis
KW - synergism
U2 - 10.1111/jen.12258
DO - 10.1111/jen.12258
M3 - Journal article
VL - 140
SP - 135
EP - 141
JO - Journal of Applied Entomology
JF - Journal of Applied Entomology
SN - 0931-2048
IS - 1-2
ER -