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Editorial: Negative and positive data, statistical power, and confidence intervals

Published online by Cambridge University Press:  15 June 2003

D. A. Andow*
Affiliation:
Department of Entomology and Center for Community Genetics, University of Minnesota, St. Paul, MN 55108, USA

Abstract

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What is negative about negative data? Scientists understand negative data from our training in data analysis and statistics, where we use a positive concept of negative data. Negative data are data that do not enable us to reject our null hypothesis. Such data are often difficult to publish because it is not possible to prove the null hypothesis. Every active research scientist has a large drawer where these data languish. In the area of environmental biosafety, however, some scientists have begun to use “negative data” in a second, normative way. This normative concept of negative data has socio-political connotations, where “negative” data has come to connote results that GMO proponents could use to support, and GMO opponents could use to oppose the development of GMOs. This politicization of GMO biosafety research is worthy of study in its own right, but EBR is prepared to accept any kind of “negative” or “positive” data.

Type
Editorial
Copyright
© ISBR, EDP Sciences, 2003

References

Arnqvist, G, Wooster, D (1995) Meta-analysis: Synthesizing research findings in ecology and evolution. Trends Ecol. Evol. 10: 236-240 CrossRef
Bourguet, D, Chaufaux, J, Micoud, A, Delos, M, Naibo, B, Bombarde, F, Marque, G, Eychenne, N, Pagliari, C (2002) Ostrinia nubilalis parasitism and the field abundance of non-target insects in transgenic Bacillus thuringiensis corn (Zea mays). Environ. Biosafety Res. 1: 49-60 CrossRef
Hill, RA, Sendashonga, C (2003) General principles for risk assessment of living modified organisms: Lessons from chemical risk assessment. Environ. Biosafety Res. 2: 81-88 CrossRef
Hoenig, JM, Heisley, DM (2001) The abuse of power: The pervasive fallacy of power calculations for data analysis. Am. Stat. 55: 19-24 CrossRef
Hurlbert, SH (1984) Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54: 187-211 CrossRef
Marvier, M (2002) Improving risk assessment for nontarget safety of transgenic crops. Ecol. Appl. 12: 1119-1124 CrossRef
Oehlert GW (2000) A first course in design and analysis of experiments. WH Freeman, New York
Ramirez, CC, Fuentes-Contreras, E, Rodriguez, LC, Niemeyer, HM (2000) Pseudoreplication and its frequency in olfactometric laboratory studies. J. Chem. Ecol. 26: 1423-1431 CrossRef
Steidl, RJ, Hayes, JP, Schauber, E (1997) Statistical power analysis in wildlife research. J. Wildl. Manag. 61: 270-279 CrossRef