Power analysis: an introduction for the life sciences:
"Oxford Biology Primers: exploring the science of life There has never been a more exciting time to be a biologist. Not only do we understand more about the biological world than ever before, but we're using that understanding in ever-more creative and valuable ways. Our understanding of t...
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| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Oxford University Press
[2021]
|
| Schriftenreihe: | Oxford biology primers
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Oxford Biology Primers: exploring the science of life There has never been a more exciting time to be a biologist. Not only do we understand more about the biological world than ever before, but we're using that understanding in ever-more creative and valuable ways. Our understanding of the way our genes work is being used to explore new ways to treat disease; our understanding of ecosystems is being used to explore more effective ways to protect the diversity of life on Earth; our understanding of plant science is being used to explore more sustainable ways to feed a growing human population. Use the Oxford Biology Primers to explore biology for yourself-to find out more about what scientists at the cutting edge of the subject are researching, and the biological problems they're trying to solve"-- |
| Beschreibung: | xi, 159 Seiten Diagramme |
| ISBN: | 9780198846635 |
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Datensatz im Suchindex
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| adam_text | Preface v About the authors vi Acknowledgements vii Introduction: Why should you read this book? Part I : Why you should want to do power analysis 1 Part 2: Why you should want to do power analyses our way 2 Why have we based the book on R and how much R will we assume you know? 3 How to use this book 3 Structure of the book 3 I. I An important preliminary: sampling and statistical testing 6 1.2 Null hypothesis statistical testing 8 1.3 Type I and Type II errors 17 1.4 How do we define statistical power? 20 2.1 With a low-powered study you risk missing interesting effects that are really there 24 2.2 You cannot read much into lack of statistical significance in a low-powered study 25 2.3 You cannot read much into statistical significance in a low-powered study 25 2.4 Your estimation of the size of an apparent effect can be unreliable in low-powered studies 30 2.5 Should you ever knowingly carry out a low-powered study? 35 3.1 The challenge posed by inherent variation 40 3.2 Are you measuring the right variables? 42 3.3 Can you measure variables more precisely? 44 3.4 Repeated measurement and subsampling to reduce inherent variation 45 3.5 Can you select experimental material so as to reduce inherent variation? 46 3.6 Can you strengthen the effect that you are interested in? 47 3.7 Can you change the design of your experiment to boost power? 51 3.8 Would you be willing to accept a higher rate of Type I error? 52 3.9 Can you increase sample size? 53 3.10 3.1 I Practical and ethical reasons why you should not always seek to further increase power 54 Case study: thinking a bit
more about how much power is enough 55
Contents 1 í 4.1 What you need to know to estimate power, and ways to produce plausible estimates of these 60 4.2 The concept of estimating power by repeated evaluation of synthetic data 63 4.3 The nuts and bolts of generating synthetic data and estimating power in a single one-factor design 67 4.4 Using simulation to compare alternative ways of doing the same experiment 74 4.5 Selecting between different alternative experiments to decide which 4.6 experiment you are actually going to do, and reporting its power 75 Case study: presenting and interpreting your power analysis 77 m : ■ ;T‘?f 5.1 Introducing our focal example 80 5.2 Think about your data frame as a way to envisage your experiment 80 5.3 Generating a simulated data set for our focal experiment 82 5.4 Comparing different designs 90 5.5 Drop-outs: using power analysis to explore the possible impact of adverse events 94 5.6 Factors with more than two levels 97 6.1 A simple linear regression 100 6.2 Beyond the straight and narrow 106 6.3 When we don’t control the values of our predictors 110 6.4 When things are not normal 112 6.5 Other distributions 115 ! Hűi ___ ___ 7.1 Several research questions in a single study 120 7.2 Designs that implicitly test multiple hypotheses 120 7.3 Conclusion 125 8.1 Likelihood of obtaining a specified precision of parameter estimation 132 8.2 Translating the concept of power across to Bayesian analysis 139 8.3 Using simulations to help with model-selection approaches 141 8.4 Exploiting your freedom in how you define study effectiveness 142
Contents Appendix: Some hand/ hints on simulating data in R Clearing out R 145 145 Naming 145 Drawing samples 146 Drawing samples of categorical data 146 Permutations and indexing 147 Generating counts 148 Generating samples of continuous variables 149 Generating correlated data 150 Glossary index 153 157
|
| adam_txt |
Preface v About the authors vi Acknowledgements vii Introduction: Why should you read this book? Part I : Why you should want to do power analysis 1 Part 2: Why you should want to do power analyses our way 2 Why have we based the book on R and how much R will we assume you know? 3 How to use this book 3 Structure of the book 3 I. I An important preliminary: sampling and statistical testing 6 1.2 Null hypothesis statistical testing 8 1.3 Type I and Type II errors 17 1.4 How do we define statistical power? 20 2.1 With a low-powered study you risk missing interesting effects that are really there 24 2.2 You cannot read much into lack of statistical significance in a low-powered study 25 2.3 You cannot read much into statistical significance in a low-powered study 25 2.4 Your estimation of the size of an apparent effect can be unreliable in low-powered studies 30 2.5 Should you ever knowingly carry out a low-powered study? 35 3.1 The challenge posed by inherent variation 40 3.2 Are you measuring the right variables? 42 3.3 Can you measure variables more precisely? 44 3.4 Repeated measurement and subsampling to reduce inherent variation 45 3.5 Can you select experimental material so as to reduce inherent variation? 46 3.6 Can you strengthen the effect that you are interested in? 47 3.7 Can you change the design of your experiment to boost power? 51 3.8 Would you be willing to accept a higher rate of Type I error? 52 3.9 Can you increase sample size? 53 3.10 3.1 I Practical and ethical reasons why you should not always seek to further increase power 54 Case study: thinking a bit
more about how much power is enough 55
Contents 1 í 4.1 What you need to know to estimate power, and ways to produce plausible estimates of these 60 4.2 The concept of estimating power by repeated evaluation of synthetic data 63 4.3 The nuts and bolts of generating synthetic data and estimating power in a single one-factor design 67 4.4 Using simulation to compare alternative ways of doing the same experiment 74 4.5 Selecting between different alternative experiments to decide which 4.6 experiment you are actually going to do, and reporting its power 75 Case study: presenting and interpreting your power analysis 77 m : ■ ;T‘?f 5.1 Introducing our focal example 80 5.2 Think about your data frame as a way to envisage your experiment 80 5.3 Generating a simulated data set for our focal experiment 82 5.4 Comparing different designs 90 5.5 Drop-outs: using power analysis to explore the possible impact of adverse events 94 5.6 Factors with more than two levels 97 6.1 A simple linear regression 100 6.2 Beyond the straight and narrow 106 6.3 When we don’t control the values of our predictors 110 6.4 When things are not normal 112 6.5 Other distributions 115 ! Hűi _ _ ' 7.1 Several research questions in a single study 120 7.2 Designs that implicitly test multiple hypotheses 120 7.3 Conclusion 125 8.1 Likelihood of obtaining a specified precision of parameter estimation 132 8.2 Translating the concept of power across to Bayesian analysis 139 8.3 Using simulations to help with model-selection approaches 141 8.4 Exploiting your freedom in how you define study effectiveness 142
Contents Appendix: Some hand/ hints on simulating data in R Clearing out R 145 145 Naming 145 Drawing samples 146 Drawing samples of categorical data 146 Permutations and indexing 147 Generating counts 148 Generating samples of continuous variables 149 Generating correlated data 150 Glossary index 153 157 |
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| spelling | Colegrave, Nick Verfasser (DE-588)135605946 aut Power analysis: an introduction for the life sciences Nick Colegrave and Graeme D. Ruxton New York Oxford University Press [2021] © 2021 xi, 159 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Oxford biology primers "Oxford Biology Primers: exploring the science of life There has never been a more exciting time to be a biologist. Not only do we understand more about the biological world than ever before, but we're using that understanding in ever-more creative and valuable ways. Our understanding of the way our genes work is being used to explore new ways to treat disease; our understanding of ecosystems is being used to explore more effective ways to protect the diversity of life on Earth; our understanding of plant science is being used to explore more sustainable ways to feed a growing human population. Use the Oxford Biology Primers to explore biology for yourself-to find out more about what scientists at the cutting edge of the subject are researching, and the biological problems they're trying to solve"-- Trennschärfe Statistik (DE-588)4603287-3 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Trennschärfe Statistik (DE-588)4603287-3 s DE-604 Ruxton, Graeme D. Verfasser (DE-588)1071366084 aut Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032512414&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Colegrave, Nick Ruxton, Graeme D. Power analysis: an introduction for the life sciences Trennschärfe Statistik (DE-588)4603287-3 gnd |
| subject_GND | (DE-588)4603287-3 (DE-588)4123623-3 |
| title | Power analysis: an introduction for the life sciences |
| title_auth | Power analysis: an introduction for the life sciences |
| title_exact_search | Power analysis: an introduction for the life sciences |
| title_exact_search_txtP | Power analysis: an introduction for the life sciences |
| title_full | Power analysis: an introduction for the life sciences Nick Colegrave and Graeme D. Ruxton |
| title_fullStr | Power analysis: an introduction for the life sciences Nick Colegrave and Graeme D. Ruxton |
| title_full_unstemmed | Power analysis: an introduction for the life sciences Nick Colegrave and Graeme D. Ruxton |
| title_short | Power analysis: an introduction for the life sciences |
| title_sort | power analysis an introduction for the life sciences |
| topic | Trennschärfe Statistik (DE-588)4603287-3 gnd |
| topic_facet | Trennschärfe Statistik Lehrbuch |
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