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Trials and tribulations of statistical significance in biochemistry and omics
Olimpio Montero1, Mikael Hedeland2, David Balgoma3
1Unidad de Excelencia, Instituto de Biología y Genética Molecular (IBGM), Universidad de Valladolid, Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain.
Abstract:
Over recent years many statisticians and researchers have highlighted that statistical inference would benefit from a better use and understanding of hypothesis testing, p-values, and statistical significance. We highlight three recommendations in the context of biochemical sciences. First recommendation: to improve the biological interpretation of biochemical data, do not use p-values (or similar test statistics) as thresholded values to select biomolecules. Second recommendation: to improve comparison among studies and to achieve robust knowledge, perform complete reporting of data. Third recommendation: statistical analyses should be reported completely with exact numbers (not as asterisks or inequalities). Owing to the high number of variables, a better use of statistics is of special importance in omic studies.
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