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Experimental Design for Laboratory Biologists : Maximising Information and Improving Reproducibility by Stanley E. Lazic (2016, Trade Paperback)
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Experimental Design for Laboratory Biologists : Maximising Information and Improving Reproducibility, Paperback by Lazic, Stanley E., ISBN 1107424887, ISBN-13 9781107424883, Brand New, Free shipping in the US
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-101107424887
ISBN-139781107424883
eBay Product ID (ePID)222108380
Product Key Features
Number of Pages422 Pages
LanguageEnglish
Publication NameExperimental Design for Laboratory Biologists : Maximising Information and Improving Reproducibility
SubjectBiostatistics, Life Sciences / Biology
Publication Year2016
TypeTextbook
Subject AreaScience, Medical
AuthorStanley E. Lazic
FormatTrade Paperback
Dimensions
Item Height0.8 in
Item Weight33.2 Oz
Item Length9.6 in
Item Width7.5 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2016-499416
ReviewsAdvance praise: 'This is a wonderfully lucid introduction to experimental design, written by an author who is clearly aware of the pitfalls that exist for the unwary experimenter. The focus is on how to design experiments to ensure reproducible research, with many examples illustrating general principles that need to be understood to avoid error and bias. The coverage of statistical analysis follows on naturally from the design issues, and is amply illustrated with exercises in R. Highly recommended.' Dorothy Bishop, University of Oxford
Dewey Edition23
IllustratedYes
Dewey Decimal570.724
Table Of Content1. Introduction: 1.1 What is reproducibility?; 1.2 The psychology of scientific discovery; 1.3 Are most published results wrong?; 1.4 Frequentist statistical interference; 1.5 Which statistics software to use?; 2. Key ideas in experimental design: 2.1 Learning versus confirming experiments; 2.2 The fundamental experimental design equation; 2.3 Randomisation; 2.4 Blocking; 2.5 Blinding; 2.6 Effect type: fixed versus random; 2.7 Factor arrangement: crossed versus nested; 2.8 Interactions between variables; 2.9 Sampling; 2.10 Use of controls; 2.11 Front-aligned versus end-aligned designs; 2.12 Heterogeneity and confounding; 3. Replication (what is 'N'?): 3.1 Biological units; 3.2 Experimental units; 3.3 Observational units; 3.4 Relationship between units; 3.5 How is the experimental unit defined in other disciplines?; 4. Analysis of common designs: 4.1 Preliminary concepts; 4.2 Background to the designs; 4.3 Completely randomised designs; 4.4 Randomised block designs; 4.5 Split-unit designs; 4.6 Repeated measures designs; 5. Planning for success: 5.1 Choosing a good outcome variable; 5.2 Power analysis and sample size calculations; 5.3 Optimal experimental designs (rules of thumb); 5.4 When to stop collecting data?; 5.5 Putting it all together; 5.6 How to get lucky; 5.7 The statistical analysis plan; 6. Exploratory data analysis: 6.1 Quality control checks; 6.2 Preprocessing; 6.3 Understanding the structure of the data; Appendix A. Introduction to R; Appendix B. Glossary.
SynopsisAn ideal resource for anyone conducting lab-based biomedical research, this guide shows how to design reproducible experiments that have low bias, high precision and widely applicable results. It explores key ideas in experimental design, including reproducibility and replication, assesses common designs, and shows how to plan for success., Specifically intended for lab-based biomedical researchers, this practical guide shows how to design experiments that are reproducible, with low bias, high precision, and widely applicable results. With specific examples from research using both cell cultures and model organisms, it explores key ideas in experimental design, assesses common designs, and shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience. An accompanying website (https: //stanlazic.github.io/EDLB.html) includes all R code, data sets, and the labstats R package. This is an ideal guide for anyone conducting lab-based biological research, from students to principle investigators working in either academia or industry, Specifically intended for lab-based biomedical researchers, this practical guide shows how to design experiments that are reproducible, with low bias, high precision, and widely applicable results. With specific examples from research using both cell cultures and model organisms, it explores key ideas in experimental design, assesses common designs, and shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience. An accompanying website (https://stanlazic.github.io/EDLB.html) includes all R code, data sets, and the labstats R package. This is an ideal guide for anyone conducting lab-based biological research, from students to principle investigators working in either academia or industry.
LC Classification NumberQH324
