Abstract
The ability to collect massive amounts of data relevant to hydrocarbon microbiology from marine and terrestrial environments continues to grow. Given the potential expense and effort of collecting this data however, it is important that careful consideration of experimental design is given before undergoing data collection to maximize the probability that experimental results will lead to statistically significant conclusions. One of the most important aspects of experimental design is the selection of appropriate number of experimental replications. The number of experimental replicates needed to achieve a desired level of statistical significance is dependent upon the known or estimated distributions of the experimental system, the statistical tool that will be used in data analysis, the size of the effect that is anticipated from the experiment, and the desired power of the statistical results. Here, protocols are presented for identifying the most appropriate number of experimental replications to achieve a desired level of statistical power.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsSpringer Nature is developing a new tool to find and evaluate Protocols. Learn more
References
Larsen P, Hamada Y et al (2012) Modeling microbial communities: current, developing, and future technologies for predicting microbial community interaction. J Biotechnol 160(1–2):17–24
Larsen PE, Gibbons SM et al (2012) Modeling microbial community structure and functional diversity across time and space. FEMS Microbiol Lett 332(2):91–98
Vidakovic B (2011) Statistics for bioengineering sciences: with MATLAB and WinBUGS support. Springer, New York
Cohen J (1988) Statistical power analysis for the behavioral sciences. L. Erlbaum Associates, Hillsdale
Huang W, Fitzmaurice GM (2005) Analysis of longitudinal data unbalanced over time. J R Stat Soc Series B Stat Methodol 67:135–155
Stegmaier J, Skanda D, Lebiedz D (2013) Robust optimal design of experiments for model discrimination using an interactive software tool. PLoS One 8(2), e55723
Field A (1998) Statistical software for microcomputers: Mlwin and nQuery advisor. Br J Math Stat Psychol 51:367–370
Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB (2013) ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-seq. PLoS One 8(7):67019. doi:10.1371/journal.pone.0067019
Friedman J, Alm EJ (2012) Inferring correlation networks from genomic survey data. PLoS Comput Biol 8(9):1002687. doi:10.1371/journal.pcbi.1002687
Holmes I, Harris K, Quince C (2012) Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One 7(2):30126. doi:10.1371/journal.pone.0030126
La Rosa PS, Brooks JP, Deych E et al (2012) Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS One 7(12):52078. doi:10.1371/journal.pone.0052078
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this protocol
Cite this protocol
Larsen, P.E. (2015). Statistical Tools for Study Design: Replication. In: McGenity, T., Timmis, K., Nogales Fernández, B. (eds) Hydrocarbon and Lipid Microbiology Protocols. Springer Protocols Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/8623_2015_95
Download citation
DOI: https://doi.org/10.1007/8623_2015_95
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49309-0
Online ISBN: 978-3-662-49310-6
eBook Packages: Springer Protocols