A guide to designing lab-based biological experiments that have low bias, high precision and widely applicable results.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Stanley E. Lazic holds a PhD in Neuroscience and a Masters degree in Computational Biology from the University of Cambridge and has conducted research at the University of Oxford, the University of Cambridge, and Harvard University, Massachusetts. He has written several papers on reproducible research and the design and analysis of biological experiments, and has published in Science and Nature. He is currently a Team Leader in Quantitative Biology (Statistics) at AstraZeneca.
Inhaltsangabe
1. 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.
1. 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.
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