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| experimental design for the life sciences: Experimental Design for the Life Sciences Graeme Ruxton, Nick Colegrave, 2011 Providing students with clear and practical advice on how best to organise experiments and collect data so as to make the subsequent analysis easier and their conclusions more robust, this text assumes no specialist knowledge. |
| experimental design for the life sciences: Experimental Design for the Life Sciences Graeme D. Ruxton, Nick Colegrave, 2006 Experimental Design for the Life Sciences teaches the reader how to effectively design experiments, to ensure that today's students are equipped with the skills they need to be the researchers of tomorrow. With a refreshingly approachable and articulate style, the book explains the essential elements of experimental design in clear, practical terms, so that the reader can grasp and apply even the most challenging concepts, including power analysis and pseudoreplication. |
| experimental design for the life sciences: Experimental Design for the Life Sciences Graeme D. Ruxton, Nick Colegrave, 2023 Providing students with clear and practical advice on how best to organise experiments and collect data so as to make the subsequent analysis easier and their conclusions more robust, this text assumes no specialist knowledge. |
| experimental design for the life sciences: Experimental Design for the Life Sciences Graeme D. Ruxton, Nick Colegrave, 2016 |
| experimental design for the life sciences: Principles of Experimental Design for the Life Sciences Murray R. Selwyn, 1996-05-23 Let this down-to-earth book be your guide to the statistical integrity of your work. Without relying on the detailed and complex mathematical explanations found in many other statistical texts, Principles of Experimental Design for the Life Sciences teaches how to design, conduct, and interpret top-notch life science studies. Learn about the planning of biomedical studies, the principles of statistical design, sample size estimation, common designs in biological experiments, sequential clinical trials, high dimensional designs and process optimization, and the correspondence between objectives, design, and analysis. Each of these important topics is presented in an understandable and non-technical manner, free of statistical jargon and formulas. Written by a biostatistical consultant with 25 years of experience, Principles of Experimental Design for the Life Sciences is filled with real-life examples from the author's work that you can quickly and easily apply to your own. These examples illustrate the main concepts of experimental design and cover a broad range of application areas in both clinical and nonclinical research. With this one innovative, helpful book you can improve your understanding of statistics, enhance your confidence in your results, and, at long last, shake off those statistical shackles! |
| experimental design for the life sciences: Experimental Design for Laboratory Biologists Stanley E. Lazic, 2016-12-08 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. |
| experimental design for the life sciences: Experimental Design for Biologists David J. Glass, 2007 The effective design of scientific experiments is critical to success, yet graduate students receive very little formal training in how to do it. Based on a well-received course taught by the author, Experimental Design for Biologistsfills this gap. Experimental Design for Biologistsexplains how to establish the framework for an experimental project, how to set up a system, design experiments within that system, and how to determine and use the correct set of controls. Separate chapters are devoted to negative controls, positive controls, and other categories of controls that are perhaps less recognized, such as “assumption controls†and “experimentalist controls†. Furthermore, there are sections on establishing the experimental system, which include performing critical “system controls†. Should all experimental plans be hypothesis-driven? Is a question/answer approach more appropriate? What was the hypothesis behind the Human Genome Project? What color is the sky? How does one get to Carnegie Hall? The answers to these kinds of questions can be found in Experimental Design for Biologists. Written in an engaging manner, the book provides compelling lessons in framing an experimental question, establishing a validated system to answer the question, and deriving verifiable models from experimental data. Experimental Design for Biologistsis an essential source of theory and practical guidance in designing a research plan. |
| experimental design for the life sciences: Experimental Design and Data Analysis for Biologists Gerald Peter Quinn, Michael J. Keough, 2002-03-21 Regression, analysis of variance, correlation, graphical. |
| experimental design for the life sciences: The Design of Experiments in Neuroscience Mary E. Harrington, 2020-02-06 Using engaging prose, Mary E. Harrington introduces neuroscience students to the principles of scientific research including selecting a topic, designing an experiment, analyzing data, and presenting research. This new third edition updates and clarifies the book's wealth of examples while maintaining the clear and effective practical advice of the previous editions. New and expanded topics in this edition include techniques such as optogenetics and conditional transgenes as well as a discussion of rigor and reproducibility in neuroscience research. Extended coverage of descriptive and inferential statistics arms readers with the analytical tools needed to interpret data. Throughout, practical guidelines are provided on avoiding experimental design problems, presenting research including creating posters and giving talks, and using a '12-step guide' to reading scientific journal articles. |
| experimental design for the life sciences: Understanding Statistics and Experimental Design Michael H. Herzog, Gregory Francis, Aaron Clarke, 2019-08-13 This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets. |
| experimental design for the life sciences: Practical Statistics and Experimental Design for Plant and Crop Science Alan G. Clewer, David H. Scarisbrick, 2013-06-17 Presents readers with a user-friendly, non-technical introductionto statistics and the principles of plant and crop experimentation.Avoiding mathematical jargon, it explains how to plan and design anexperiment, analyse results, interpret computer output and presentfindings. Using specific crop and plant case studies, this guidepresents: * The reasoning behind each statistical method is explained beforegiving relevant, practical examples * Step-by-step calculations with examples linked to three computerpackages (MINITAB, GENSTAT and SAS) * Exercises at the end of many chapters * Advice on presenting results and report writing Written by experienced lecturers, this text will be invaluable toundergraduate and postgraduate students studying plant sciences,including plant and crop physiology, biotechnology, plant pathologyand agronomy, plus ecology and environmental science students andthose wanting a refresher or reference book in statistics. |
| experimental design for the life sciences: Design of Experiments for Agriculture and the Natural Sciences Reza Hoshmand, 2018-10-03 Written to meet the needs of both students and applied researchers, Design of Experiments for Agriculture and the Natural Sciences, Second Edition serves as an introductory guide to experimental design and analysis. Like the popular original, this thorough text provides an understanding of the logical underpinnings of design and analysis by selecting and discussing only those carefully chosen designs that offer the greatest utility. However, it improves on the first edition by adhering to a step-by-step process that greatly improves accessibility and understanding. Real problems from different areas of agriculture and science are presented throughout to show how practical issues of design and analysis are best handled. Completely revised to greatly enhance readability, this new edition includes: A new chapter on covariance analysis to help readers reduce errors, while enhancing their ability to examine covariances among selected variables Expanded material on multiple regression and variance analysis Additional examples, problems, and case studies A step-by-step Minitab® guide to help with data analysis Intended for those in the agriculture, environmental, and natural science fields as well as statisticians, this text requires no previous exposure to analysis of variance, although some familiarity with basic statistical fundamentals is assumed. In keeping with the book's practical orientation, numerous workable problems are presented throughout to reinforce the reader's ability to creatively apply the principles and concepts in any given situation. |
| experimental design for the life sciences: Experimental Design S.N. Deming, S.L. Morgan, 1987-01-01 Now available in a paperback edition is a book which has been described as ``...an exceptionally lucid, easy-to-read presentation... would be an excellent addition to the collection of every analytical chemist. I recommend it with great enthusiasm.'' (Analytical Chemistry). Unlike most current textbooks, it approaches experimental design from the point of view of the experimenter, rather than that of the statistician. As the reviewer in `Analytical Chemistry' went on to say: ``Deming and Morgan should be given high praise for bringing the principles of experimental design to the level of the practicing analytical chemist.''.The book first introduces the reader to the fundamentals of experimental design. Systems theory, response surface concepts, and basic statistics serve as a basis for the further development of matrix least squares and hypothesis testing. The effects of different experimental designs and different models on the variance-covariance matrix and on the analysis of variance (ANOVA) are extensively discussed. Applications and advanced topics (such as confidence bands, rotatability, and confounding) complete the text. Numerous worked examples are presented.The clear and practical approach adopted by the authors makes the book applicable to a wide audience. It will appeal particularly to those with a practical need (scientists, engineers, managers, research workers) who have completed their formal education but who still need to know efficient ways of carrying out experiments. It will also be an ideal text for advanced undergraduate and graduate students following courses in chemometrics, data acquisition and treatment, and design of experiments. |
| experimental design for the life sciences: An Introduction To Experimental Design And Statistics For Biology David Heath, 1995-10-26 This illustrated textbook for biologists provides a refreshingly clear and authoritative introduction to the key ideas of sampling, experimental design, and statistical analysis. The author presents statistical concepts through common sense, non-mathematical explanations and diagrams. These are followed by the relevant formulae and illustrated by w |
| experimental design for the life sciences: NERD – New Experimental Research in Design Michael Erlhoff, Wolfgang Jonas, 2018-11-05 Design has long expressed and established itself as an independent research competence – a fact that also companies, institutions and politicians have come to acknowledge. What is still needed, however, is a stronger public platform for design to confidently reflect upon this process and to establish and communicate the specific innovative and experimental dimension of design research. For this reason, BIRD, the Board of International Research in Design, has developed the New Experimental Research in Design / NERD format. The edited conference contributions of twelve young researchers from all over the world provide an impressive and diverse and insightful range of intelligent and inspiring approaches in design research, giving rise to further debate and action in the rapidly evolving field. |
| experimental design for the life sciences: Statistical Design and Analysis of Biological Experiments Hans-Michael Kaltenbach, 2021-04-15 This richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving precision. In addition, it addresses experiments with large numbers of treatment factors and response surface methods for optimizing experimental conditions or biotechnological yields. The book emphasizes the estimation of effect sizes and the principled use of statistical arguments in the broader scientific context. It gradually transitions from classical analysis of variance to modern linear mixed models, and provides detailed information on power analysis and sample size determination, including ‘portable power’ formulas for making quick approximate calculations. In turn, detailed discussions of several real-life examples illustrate the complexities and aberrations that can arise in practice. Chiefly intended for students, teachers and researchers in the fields of experimental biology and biomedicine, the book is largely self-contained and starts with the necessary background on basic statistical concepts. The underlying ideas and necessary mathematics are gradually introduced in increasingly complex variants of a single example. Hasse diagrams serve as a powerful method for visualizing and comparing experimental designs and deriving appropriate models for their analysis. Manual calculations are provided for early examples, allowing the reader to follow the analyses in detail. More complex calculations rely on the statistical software R, but are easily transferable to other software. Though there are few prerequisites for effectively using the book, previous exposure to basic statistical ideas and the software R would be advisable. |
| experimental design for the life sciences: Research Methodology in the Social, Behavioural and Life Sciences Herman J Ader, Gideon J Mellenbergh, 1999-12-07 This is an ideal text for advanced courses in research methods and experimental design. It argues that the methodology of quantitative research is a unified discipline with basic notions, procedures and ways of reasoning which can be applied across the social, behavioural and life sciences. Key designs, models and methods in research are covered by leading contributors in their field who seek to explain the fundamentals of the research process to enable the student to understand the broader implications and unifying themes. |
| experimental design for the life sciences: Experimental Design Research Philip Cash, Tino Stanković, Mario Štorga, 2016-05-17 This book presents a new, multidisciplinary perspective on and paradigm for integrative experimental design research. It addresses various perspectives on methods, analysis and overall research approach, and how they can be synthesized to advance understanding of design. It explores the foundations of experimental approaches and their utility in this domain, and brings together analytical approaches to promote an integrated understanding. The book also investigates where these approaches lead to and how they link design research more fully with other disciplines (e.g. psychology, cognition, sociology, computer science, management). Above all, the book emphasizes the integrative nature of design research in terms of the methods, theories, and units of study—from the individual to the organizational level. Although this approach offers many advantages, it has inherently led to a situation in current research practice where methods are diverging and integration between individual, team and organizational understanding is becoming increasingly tenuous, calling for a multidisciplinary and transdiscipinary perspective. Experimental design research thus offers a powerful tool and platform for resolving these challenges. Providing an invaluable resource for the design research community, this book paves the way for the next generation of researchers in the field by bridging methods and methodology. As such, it will especially benefit postgraduate students and researchers in design research, as well as engineering designers. |
| experimental design for the life sciences: Statistics Explained Steve McKillup, 2011-11-03 An understanding of statistics and experimental design is essential for life science studies, but many students lack a mathematical background and some even dread taking an introductory statistics course. Using a refreshingly clear and encouraging reader-friendly approach, this book helps students understand how to choose, carry out, interpret and report the results of complex statistical analyses, critically evaluate the design of experiments and proceed to more advanced material. Taking a straightforward conceptual approach, it is specifically designed to foster understanding, demystify difficult concepts and encourage the unsure. Even complex topics are explained clearly, using a pictorial approach with a minimum of formulae and terminology. Examples of tests included throughout are kept simple by using small data sets. In addition, end-of-chapter exercises, new to this edition, allow self-testing. Handy diagnostic tables help students choose the right test for their work and remain a useful refresher tool for postgraduates. |
| experimental design for the life sciences: Methods of Randomization in Experimental Design Valentim R. Alferes, 2012-10 This text provides a conceptual systematization and a practical tool for the randomization of between-subjects and within-subjects experimental designs. |
| experimental design for the life sciences: Experiments C. F. Jeff Wu, Michael S. Hamada, 2011-09-20 Praise for the First Edition: If you . . . want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library. —Journal of the American Statistical Association Fully updated to reflect the major progress in the use of statistically designed experiments for product and process improvement, Experiments, Second Edition introduces some of the newest discoveries—and sheds further light on existing ones—on the design and analysis of experiments and their applications in system optimization, robustness, and treatment comparison. Maintaining the same easy-to-follow style as the previous edition while also including modern updates, this book continues to present a new and integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, reliability improvement, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Along with a new chapter that focuses on regression analysis, the Second Edition features expanded and new coverage of additional topics, including: Expected mean squares and sample size determination One-way and two-way ANOVA with random effects Split-plot designs ANOVA treatment of factorial effects Response surface modeling for related factors Drawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study. Experiments, Second Edition is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians. |
| experimental design for the life sciences: Fundamentals of Statistical Experimental Design and Analysis Robert G. Easterling, 2015-09-08 Professionals in all areas – business; government; the physical, life, and social sciences; engineering; medicine, etc. – benefit from using statistical experimental design to better understand their worlds and then use that understanding to improve the products, processes, and programs they are responsible for. This book aims to provide the practitioners of tomorrow with a memorable, easy to read, engaging guide to statistics and experimental design. This book uses examples, drawn from a variety of established texts, and embeds them in a business or scientific context, seasoned with a dash of humor, to emphasize the issues and ideas that led to the experiment and the what-do-we-do-next? steps after the experiment. Graphical data displays are emphasized as means of discovery and communication and formulas are minimized, with a focus on interpreting the results that software produce. The role of subject-matter knowledge, and passion, is also illustrated. The examples do not require specialized knowledge, and the lessons they contain are transferrable to other contexts. Fundamentals of Statistical Experimental Design and Analysis introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs: Completely Randomized designs, with single or multiple treatment factors, quantitative or qualitative Randomized Block designs Latin Square designs Split-Unit designs Repeated Measures designs Robust designs Optimal designs Written in an accessible, student-friendly style, this book is suitable for a general audience and particularly for those professionals seeking to improve and apply their understanding of experimental design. |
| experimental design for the life sciences: Deep Learning for the Life Sciences Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, 2019-04-10 Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working |
| experimental design for the life sciences: Experimental Design and Analysis Steven R. Brown, Lawrence E. Melamed, 1990 Experimental design is one of the most fundamental topics in social science statistics. This book introduces the reader to the elements of experimental design and analysis through careful explanations of the procedures as well as through illustrations using actual examples. |
| experimental design for the life sciences: Modern Experimental Design Thomas P. Ryan, 2006-12-22 A complete and well-balanced introduction to modern experimental design Using current research and discussion of the topic along with clear applications, Modern Experimental Design highlights the guiding role of statistical principles in experimental design construction. This text can serve as both an applied introduction as well as a concise review of the essential types of experimental designs and their applications. Topical coverage includes designs containing one or multiple factors, designs with at least one blocking factor, split-unit designs and their variations as well as supersaturated and Plackett-Burman designs. In addition, the text contains extensive treatment of: Conditional effects analysis as a proposed general method of analysis Multiresponse optimization Space-filling designs, including Latin hypercube and uniform designs Restricted regions of operability and debarred observations Analysis of Means (ANOM) used to analyze data from various types of designs The application of available software, including Design-Expert, JMP, and MINITAB This text provides thorough coverage of the topic while also introducing the reader to new approaches. Using a large number of references with detailed analyses of datasets, Modern Experimental Design works as a well-rounded learning tool for beginners as well as a valuable resource for practitioners. |
| experimental design for the life sciences: Optimal Experimental Design with R Dieter Rasch, Jurgen Pilz, L.R. Verdooren, Albrecht Gebhardt, 2011-05-18 Experimental design is often overlooked in the literature of applied and mathematical statistics: statistics is taught and understood as merely a collection of methods for analyzing data. Consequently, experimenters seldom think about optimal design, including prerequisites such as the necessary sample size needed for a precise answer for an experi |
| experimental design for the life sciences: Experiment Design and Statistical Methods For Behavioural and Social Research David R. Boniface, 2019-05-20 Experiment Design and Statistical Methods introduces the concepts, principles, and techniques for carrying out a practical research project either in real world settings or laboratories - relevant to studies in psychology, education, life sciences, social sciences, medicine, and occupational and management research. The text covers: repeated measures unbalanced and non-randomized experiments and surveys choice of design adjustment for confounding variables model building and partition of variance covariance multiple regression Experiment Design and Statistical Methods contains a unique extension of the Venn diagram for understanding non-orthogonal design, and it includes exercises for developing the reader's confidence and competence. The book also examines advanced techniques for users of computer packages or data analysis, such as Minitab, SPSS, SAS, SuperANOVA, Statistica, BMPD, SYSTAT, Genstat, and GLIM. |
| experimental design for the life sciences: Applied Statistical Methods in Agriculture, Health and Life Sciences Bayo Lawal, 2014-09-15 This textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R. This textbook is developed for undergraduate students in agriculture, nursing, biology and biomedical research. Graduate students will also find it to be a useful way to refresh their statistics skills and to reference software options. The unique combination of examples is approached using MINITAB and R for their individual strengths. Subjects covered include among others data description, probability distributions, experimental design, regression analysis, randomized design and biological assay. Unlike other biostatistics textbooks, this text also includes outliers, influential observations in regression and an introduction to survival analysis. Material is taken from the author's extensive teaching and research in Africa, USA and the UK. Sample problems, references and electronic supplementary material accompany each chapter. |
| experimental design for the life sciences: Encyclopedia of Research Design Neil J. Salkind, 2010-06-22 Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases.--Publisher's description. |
| experimental design for the life sciences: Best Practices in Quantitative Methods Jason W. Osborne, 2008 The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the best choice—or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques: The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience: Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods. |
| experimental design for the life sciences: Experimental and Quasi-Experimental Designs for Research Donald T. Campbell, Julian C. Stanley, 2015-09-03 We shall examine the validity of 16 experimental designs against 12 common threats to valid inference. By experiment we refer to that portion of research in which variables are manipulated and their effects upon other variables observed. It is well to distinguish the particular role of this chapter. It is not a chapter on experimental design in the Fisher (1925, 1935) tradition, in which an experimenter having complete mastery can schedule treatments and measurements for optimal statistical efficiency, with complexity of design emerging only from that goal of efficiency. Insofar as the designs discussed in the present chapter become complex, it is because of the intransigency of the environment: because, that is, of the experimenter’s lack of complete control. |
| experimental design for the life sciences: Reproducibility and Replicability in Science National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Board on Research Data and Information, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Division on Earth and Life Studies, Nuclear and Radiation Studies Board, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Board on Behavioral, Cognitive, and Sensory Sciences, Committee on Reproducibility and Replicability in Science, 2019-10-20 One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science. |
| experimental design for the life sciences: Experiments in Ecology A. J. Underwood, 1997 First published in 1996, this book is a logical and consistent approach to experimental design using statistical principles. |
| experimental design for the life sciences: Quasi-Experimental Research Designs Bruce A. Thyer, 2012-02-16 The role of group research designs to evaluate social work practice -- Pre-experimental group research designs -- Quasi-experimental group research designs -- Time-series research designs -- Evaluating and reporting quasi-experimental studies. |
| experimental design for the life sciences: Experimental Evolution Theodore Garland, Michael R. Rose, 2009-12-03 This volume summarizes studies in experimental evolution, outlining current techniques and applications, and presenting the field's range of research. |
| experimental design for the life sciences: Power Analysis Nick Colegrave, Graeme D. Ruxton, 2020-11-17 Written primarily for mid-to-upper level undergraduates, this compelling introduction to power analysis offers a clear, conceptual understanding of the factors that influence statistical power, as well as guidance on improving and presenting the outcomes of power analyses to justify experimental design decisions. |
| experimental design for the life sciences: Experimental Design Paul D. Berger, Robert E. Maurer, Giovana B. Celli, 2017-11-28 This text introduces and provides instruction on the design and analysis of experiments for a broad audience. Formed by decades of teaching, consulting, and industrial experience in the Design of Experiments field, this new edition contains updated examples, exercises, and situations covering the science and engineering practice. This text minimizes the amount of mathematical detail, while still doing full justice to the mathematical rigor of the presentation and the precision of statements, making the text accessible for those who have little experience with design of experiments and who need some practical advice on using such designs to solve day-to-day problems. Additionally, an intuitive understanding of the principles is always emphasized, with helpful hints throughout. |
| experimental design for the life sciences: The Design of Experiments in Neuroscience Mary Harrington, 2011 Originally published in 2006, the second edition of The Design of Experiments in Neuroscience continues to be an excellent and eminently readable guideline for students beginning their scientific careers. Although all of the examples are specific to neuroscience, this slender volume offers valuable illumination on core practices, principles, and experimental approaches pertinent for all new researchers. Chapter topics cover recognizing pseudoscience, ethics, how to critically read journal articles, how to pick an experimental question, basic research design, controlling variables, and tips for becoming an independent investigator. Each of the eight chapters provides descriptive figures and extra information boxes, questions to check reader comprehension, additional thought questions, further reading suggestions, and Web resources. The six appendixes are as valuable as the main text, including information on working with data, writing research papers, a sample paper, questions and exercises for review, a glossary, and answers to chapter questions. Neuroscientist Harrington (Smith College) has created a wonderful resource that should be a must read for every neuroscientist in training, if not all novice scientists. Summing Up: Highly recommended. Upper-division undergraduates and graduate students. Upper-division Undergraduates; Graduate Students. Reviewed by C. L. Iwema. |
| experimental design for the life sciences: An Introduction to Statistical Analysis in Research Kathleen F. Weaver, Vanessa C. Morales, Sarah L. Dunn, Kanya Godde, Pablo F. Weaver, 2017-09-05 Provides well-organized coverage of statistical analysis and applications in biology, kinesiology, and physical anthropology with comprehensive insights into the techniques and interpretations of R, SPSS®, Excel®, and Numbers® output An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences develops a conceptual foundation in statistical analysis while providing readers with opportunities to practice these skills via research-based data sets in biology, kinesiology, and physical anthropology. Readers are provided with a detailed introduction and orientation to statistical analysis as well as practical examples to ensure a thorough understanding of the concepts and methodology. In addition, the book addresses not just the statistical concepts researchers should be familiar with, but also demonstrates their relevance to real-world research questions and how to perform them using easily available software packages including R, SPSS®, Excel®, and Numbers®. Specific emphasis is on the practical application of statistics in the biological and life sciences, while enhancing reader skills in identifying the research questions and testable hypotheses, determining the appropriate experimental methodology and statistical analyses, processing data, and reporting the research outcomes. In addition, this book: • Aims to develop readers’ skills including how to report research outcomes, determine the appropriate experimental methodology and statistical analysis, and identify the needed research questions and testable hypotheses • Includes pedagogical elements throughout that enhance the overall learning experience including case studies and tutorials, all in an effort to gain full comprehension of designing an experiment, considering biases and uncontrolled variables, analyzing data, and applying the appropriate statistical application with valid justification • Fills the gap between theoretically driven, mathematically heavy texts and introductory, step-by-step type books while preparing readers with the programming skills needed to carry out basic statistical tests, build support figures, and interpret the results • Provides a companion website that features related R, SPSS, Excel, and Numbers data sets, sample PowerPoint® lecture slides, end of the chapter review questions, software video tutorials that highlight basic statistical concepts, and a student workbook and instructor manual An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences is an ideal textbook for upper-undergraduate and graduate-level courses in research methods, biostatistics, statistics, biology, kinesiology, sports science and medicine, health and physical education, medicine, and nutrition. The book is also appropriate as a reference for researchers and professionals in the fields of anthropology, sports research, sports science, and physical education. KATHLEEN F. WEAVER, PhD, is Associate Dean of Learning, Innovation, and Teaching and Professor in the Department of Biology at the University of La Verne. The author of numerous journal articles, she received her PhD in Ecology and Evolutionary Biology from the University of Colorado. VANESSA C. MORALES, BS, is Assistant Director of the Academic Success Center at the University of La Verne. SARAH L. DUNN, PhD, is Associate Professor in the Department of Kinesiology at the University of La Verne and is Director of Research and Sponsored Programs. She has authored numerous journal articles and received her PhD in Health and Exercise Science from the University of New South Wales. KANYA GODDE, PhD, is Assistant Professor in the Department of Anthropology and is Director/Chair of Institutional Review Board at the University of La Verne. The author of numerous journal articles and a member of the American Statistical Association, she received her PhD in Anthropology from the University of Tennessee. PABLO F. WEAVER, PhD, is Instructor in the Department of Biology at the University of La Verne. The author of numerous journal articles, he received his PhD in Ecology and Evolutionary Biology from the University of Colorado. |
| experimental design for the life sciences: Methods in Psychological Research Bryan J. Rooney, Annabel Ness Evans, 2018-08-01 Methods in Psychological Research introduces students to the rich world of research in psychology through student-friendly writing, compelling real-world examples, and frequent opportunities for practice. Using a relaxed yet supportive tone that eases student anxiety, the authors present a mixture of conceptual and practical discussions, and spark reader interest in research by covering meaningful topics that resonate with today’s students. In-text features like Conceptual Exercises, FYI sections, and FAQ sections with accompanying visual cues support learning throughout the research experience. The Fourth Edition equips students with the tools they need to understand research concepts, conduct their own experiments, and present their findings. |
Experimental Design for the Life Sciences (2 Edition) - Sci…
Rather than being a book in which one dips to search for the answer to a particular question or for a desired fact, Experimental Design for the Life …
Experimental design for the life sciences - dandelon.com
1.1 Why experiments need to be designed. 1.2 The costs of poor design. 1.2.1 Time and money. 1.2.2 Ethical issues. 1.3 The relationship between …
Topic 1: INTRODUCTION TO PRINCIPLES OF EXPERIMEN…
experimental design in the following diagram (Box et al., 1978), is represented by a movable window through which certain aspects of the …
Chapter 4 Experimental Designs and Their Analysis - II…
Design of experiment: One of the main objectives of designing an experiment is how to verify the hypothesis in an efficient and economical way. In the …
Experimental design for the Life Sciences - Taylor & Franc…
Experimental design for the Life Sciences (2nd Edition) By Graeme D. Ruxton and Nick Colgrave. 162pp., Oxford University Press, 2006, ISBN …
Experimental Design for the Life Sciences (2 Edition) - Science …
Rather than being a book in which one dips to search for the answer to a particular question or for a desired fact, Experimental Design for the Life Sciences is a book to read through in its …
Experimental design for the life sciences - dandelon.com
1.1 Why experiments need to be designed. 1.2 The costs of poor design. 1.2.1 Time and money. 1.2.2 Ethical issues. 1.3 The relationship between experimental design and statistics. 1.4 Why …
Topic 1: INTRODUCTION TO PRINCIPLES OF EXPERIMENTAL DESIGN …
experimental design in the following diagram (Box et al., 1978), is represented by a movable window through which certain aspects of the true state of nature, more or less distorted by …
Chapter 4 Experimental Designs and Their Analysis - IIT Kanpur
Design of experiment: One of the main objectives of designing an experiment is how to verify the hypothesis in an efficient and economical way. In the contest of the null hypothesis of equality …
Experimental design for the Life Sciences - Taylor & Francis …
Experimental design for the Life Sciences (2nd Edition) By Graeme D. Ruxton and Nick Colgrave. 162pp., Oxford University Press, 2006, ISBN p78-0-19-928511-2 (Pbk), £17.99. This is an …
Chapter 10, Experimental Designs - University of British Columbia
Experimental design is a term describing the logical structure of an experiment. Let us begin by defining some of the terms that are commonly used in discussions of experimental design. The …
Experimental Design in Life Sciences - sefhouston.org
The world around us is teeming with organisms that are easy to find and simple to grow and study. Encourage your student to observe the life around them and ask how organisms …
Systems biology: experimental design - FEBS Press
Experimental design has a long tradition in statistics, engineering and life sciences, dating back to the beginning of the last century when optimal designs for industrial and agricultural trials were …
Experimental design for the life sciences - GBV
UNIVERSITY PRESS. Contents. 1 Why you need to care about design. 1.1 Why experiments need to be designed. 1.2 The costs of poor design. 1.2.1 Time and money. 1.2.2 Ethical …
Experimental design and sample size determination
Experimental design 3 Basic principles 1.Formulate question/goal in advance 2.Comparison/control 3.Replication 4.Randomization 5.Stratification (aka blocking) 6.Factorial …
Reporting Life Sciences Research - Nature
Reporting Experimental Design. Sample size: When confirming an effect of known size, it is considered best practice to estimate before conducting the experiments what sample size is …
Design and Analysis of Experiments - هيئة التدريس جامعة ...
experimental design early in the product cycle can substantially reduce development lead time and cost, leading to processes and products that perform better in the field and have higher …
Topic 1: Introduction to the Principles of Experimental Design
Experimental design concerns the validity and efficiency of the experiment. The experimental design in the following diagram (Box et al., 1978) is represented by a movable
The Experimental Life Sciences in the Twentieth Century - JSTOR
To unify our study of the rise of the experimental life sciences and make it easier to understand, we have imposed a common analytic framework on the disparate fields that constitute them.
Laboratory Courses with Guided-Inquiry Modules Improve …
10 Aug 2018 · Laboratory Courses with Guided-Inquiry Modules Improve Scientific Reasoning and Experimental Design Skills for the Least-Prepared Undergraduate Students. Lawrence S. …
Design of Experiments in Ecological and Environmental Problems: …
proposing research to study life stage and/or genetic susceptibility in order to better characterize sources of human variability in response to chemical exposure.
Experimental and quasi-experimental designs:
Experimental design –protocol for data collection with the aim to establish causality between treatment and changes in the dependent variable; a key consideration is how participants are …
Chapter 13 Experimental Design for Plant Improvement - Springer
Replication, randomisation and blocking are fundamental experimental design concepts required for rigorous plant improvement experiments. Understanding and minimizing bias and pseudo …
School of Environmental and Life Sciences ENVS2320: Experimental Design …
Leading on from the 1st year statistics, this course examines the current best practice techniques in experimental ecology. Topics covered, include the design and implementation of …
Life Sciences Reporting Summary - University of Notre Dame
This form is intended for publication with all accepted life science papers and provides structure for consistency and transparency in reporting. Every life science submission will use this form; …
