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Comparing and Combining Data across Studies: Alternatives to Significance Testing

Eduardo Fernandez-Duque
Oikos
Vol. 79, No. 3 (Sep., 1997), pp. 616-618
Published by: Wiley on behalf of Nordic Society Oikos
DOI: 10.2307/3546906
Stable URL: http://www.jstor.org/stable/3546906
Page Count: 3
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Comparing and Combining Data across Studies: Alternatives to Significance Testing
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Abstract

The need to compare and combine data quantitatively is becoming more frequent in studies of animal behaviour, ecology and conservation. Using a hypothetical data set, I point out some limitations of combining and comparing data using Null Hypothesis Significance Testing (NHST). First, I discuss three different aspects of data analysis that should regularly be considered: (1) effect size estimation, (2) confidence intervals estimation and, (3) power analysis. I then suggest meta-analysis as a sensible alternative method to account for some limitations of NHST. Meta-analysis is a quantitative technique for the combination and comparison of independent but similar studies. Meta-analysis allows comparison and summary of effect sizes across studies. When testing hypotheses framed in evolutionary theory, where small effects may have profound consequences, a knowledge of the magnitude of the association may be as important as knowing whether the data comply with the arbitrary, sacred and dogmatic significance criterion of p < 0.05.

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