Biological Importance and Statistical Significance.
Summary of "Biological Importance and Statistical Significance."
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 dataset depends on the experimental design, that no amount of statistical sophistication can rescue a badly designed study, and 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.
This article was published in the following journal.
Name: Journal of agricultural and food chemistry
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Medical and Biotech [MESH] Definitions
Biological systems as affected by time. Aging, biological rhythms, and cyclic phenomena are included. Statistical, computer-aided mathematical procedures are used to describe, in mathematical terminology, various biological functions over time.
A multidisciplinary field of research and practice studying the periodicity of biological systems and the application of principles of chronobiology to various therapeutic strategies. Aging, biological rhythms, and cyclic phenomena are included. Statistical, computer-aided mathematical procedures are used to describe, in mathematical terminology, various biological functions over time.
The use of statistical and mathematical methods to analyze biological observations and phenomena.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.