8 Ortiz Conclusions <strong>The</strong> decrease of the sampling variability (coefficient of variability) is definitively an excellent and useful strategy <strong>to</strong> be applied when the magnitude of effect size is small and, therefore, a large sample size is required. However, this procedure must be applied once the size of the perturbation is known, not before. <strong>The</strong>refore, any sampling program design should be avoided that could have deleterious consequences for natural systems, especially when the acceptance of the null hypothesis was incorrect! This situation could support misguided management plans, as was described extensively by Peterman (1990a). In conclusion, the magnitude of perturbation (effect size) is the most relevant information for any hypothesis testing and more efforts must be focused <strong>to</strong>wards its estimation (Rotenberry & Wiens, 1985). Acknowledgements I would like <strong>to</strong> thank Prof. Dr. M. Wolff, M.Sc. C. Jimenez and Dr. S. Jesse and the anonymous reviewers for criticising and improving the manuscript. References Andrew, N. & B. Maps<strong>to</strong>ne, 1987: Sampling and the description of spatial pattern in marine ecology. Oceanogr. Mar. Biol. Annu. Rev., 25: 39±90. Bernstein, B. & J. Zalinski, 1983: An optimum sampling design and power tests for environmental biologists. J. Environ. Manage., 16: 35±43. Cohen, J., 1988: Statistical power analysis for the behavioral sciences. 2 nd edition. L. Erlbaum Associates, Hillsdale, N.Y.; 567 pp. Dixon, W. & F. Massey, 1969: Introduction <strong>to</strong> statistical analysis. 3 rd edition. McGraw Hill Book Co., N.Y.; 638 pp. Gerrodette, T., 1987: A power analysis for detecting trends. Ecology, 68(5): 1364±1372. Gray, J., 1996: Environmental science and a precautionary approach revisited. Mar. Pollut. Bull., 32(7): 532± 534. Green, R., 1989: Power analysis and practical strategies for environmental moni<strong>to</strong>ring. Environ. Res., 50: 195±205. Maps<strong>to</strong>ne, B., 1995: Scalable decision rules for environmental impacts studies: Effect size, Type I and Type II errors. Ecol. Appl., 5(2): 401±410. Mouillot, D., J.-M. Culioli, A. Leprete & J.-A. Tomasini, 1999: Dispersion statistics for three fish species (Symphodus ocellatus, Serranus scriba and Diplodus annularis) in the Lavezzi Islands Marine Reserve (South Corsica, Mediterranean Sea). P.S.Z.N.: Marine Ecology, 20(1): 19±34. Peterman, R., 1990a: Statistical power analysis can improve fisheries research and management. Can. J. Aquat. Sci., 47: 1±15. Peterman, R., 1990b: <strong>The</strong> importance of reporting statistical power: the forest decline and acidic deposition example. Ecology, 71(5): 2024±2027. Peterman, R. & M. M'Gonigle, 1992: Statistical power analysis and the precautionary principle. Mar. Pollut. Bull., 24(5): 231±234. Ribic, Ch. & L. Ganio, 1996: Power analysis for beach surveys of marine debris. Mar. Pollut. Bull., 32(7): 554±557. Rotenberry, J. & J. Wiens, 1985: Statistical Power Analysis and community-wide patterns. Am. Nat., 125: 164±168. Schlese, W. & W. Nelson, 1996: A power analysis of methods for assessment of change in seagrass cover. Aquat. Bot., 53: 227±233. Sheppard, Ch., 1999: How large should my sample be? Some quick guides <strong>to</strong> sample size and the power of test. Mar. Pollut. Bull., 38(6): 439±447. Sokal, R. & F. Rohlf, 1995: Biometry. 3 rd edition. W.H. Freeman and Co., San Francisco; 878 pp. Tiku, M., W. Tan & N. Balakrishnan, 1986: Robust Inference. Marcel Dekker, Inc., N.Y.; 321 pp.
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