title: Biological importance and statistical significance.
abstract: Statistical ideas behind the analysis of experiments related to crop composition and the genetic factors underlying composition are discussed. The emphasis is on concepts rather than statistical formulations. Statistical analysis and biological considerations are shown to be complementary rather than contradictory, in that the statistical analysis of a data set depends on the experimental design, that no amount of statistical sophistication can rescue a badly designed study, and that good experimental design is crucial. The traditional null hypothesis significance testing approach has severe limitations, but p values and statistical significance still often seem to be the primary objective of an analysis. Emphasis instead should be on identifying the size of effects that are biologically important and, with the involvement of the "domain" scientist, using these to help design experiments with appropriate sample sizes and statistical power. The issues discussed here are also directly applicable to other areas of research.
authors.affiliation: St. George's, University of London, Cranmer Terrace, London SW17 0RE, United Kingdom.
authors.forename: David P
authors.initials: DP
authors.lastname: Lovell
mesh_terms: D001499:Bayes Theorem; D001682:Biological Availability; D016001:Confidence Intervals; D018556:Crops, Agricultural / Q000737:chemistry* / Q000235:genetics*; D008954:Models, Biological; D015999:Multivariate Analysis; D030821:Plants, Genetically Modified / Q000737:chemistry; D012107:Research Design / Q000706:statistics & numerical data*; D013223:Statistics as Topic