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| null hypothesis biology example: Conceptual Issues in Ecology Esa. Saarinen, 2012-12-06 In this collection of essays, some of the leading ecologists and philosophers discuss the foundations of ecology and evolutionary biology. While large scale philosophical convictions and attitudes often direct the theorist's line of concrete action in data collection and in theory information, the founda tional convictions typically remain tacit, and are seldom argued for. The present collection aims to remedy this situation. It brings together scholars representing different approaches in a joint effort to explicate and analyse some of the key issues underlying ecological theorizing, be they conceptual, epistemological or ontological. The bulk of the present collection is reprinted from Synthese 43 (1980). William C. Wimsatt's paper 'Reductionistic Research Strategies and Their Biases in the Units of Selection Controversy' is in turn reprinted from T. Nickles (ed.) Scientific Discovery: Case Studies (D. Reidel, 1980). It appears here with the kind permission of Prof. Nickles. The publisher's consent for the reprints has been in each case automatic. The essays of Y rjo Haila and Olli Jarvinen, and of Leigh M. Van Valen appear here for the first time. In bringing the present collection together, as well as in editing the Synthese symposium on which it is based, I have greatly benefited from the suggestions of Professors Marjorie Grene, Olli J iirvinen and Daniel Simberloff. In addition to them, I wish to thank all the contributors for their interest in this project. |
| null hypothesis biology example: Invasion Biology Jonathan Jeschke, Tina Heger, 2022-01-10 There are many hypotheses describing the interactions involved in biological invasions, but it is largely unknown whether they are backed up by empirical evidence. This book fills that gap by developing a tool for assessing research hypotheses and applying it to a number of invasion hypotheses, using the hierarchy-of-hypotheses (HoH) approach, and mapping the connections between theory and evidence. In Part 1, an overview chapter of invasion biology is followed by an introduction to the HoH approach and short chapters by science theorists and philosophers that comment on the approach. Part 2 outlines the invasion hypotheses and their interrelationships. These include biotic resistance and island susceptibility hypotheses, disturbance hypothesis, invasional meltdown hypothesis, enemy release hypothesis, evolution of increased competitive ability and shifting defence hypotheses, tens rule, phenotypic plasticity hypothesis, Darwin's naturalization & limiting similarity hypotheses and the propagule pressure hypothesis. Part 3 suggests future directions for invasion research. |
| null hypothesis biology example: Biostatistical Design and Analysis Using R Dr Murray Logan, 2011-09-20 R — the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research. Topics covered include: simple hypothesis testing, graphing exploratory data analysis and graphical summaries regression (linear, multi and non-linear) simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures) frequency analysis and generalized linear models. Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques. The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links. |
| null hypothesis biology example: Statistics from A to Z Andrew A. Jawlik, 2016-10-24 Statistics is confusing, even for smart, technically competent people. And many students and professionals find that existing books and web resources don’t give them an intuitive understanding of confusing statistical concepts. That is why this book is needed. Some of the unique qualities of this book are: • Easy to Understand: Uses unique “graphics that teach” such as concept flow diagrams, compare-and-contrast tables, and even cartoons to enhance “rememberability.” • Easy to Use: Alphabetically arranged, like a mini-encyclopedia, for easy lookup on the job, while studying, or during an open-book exam. • Wider Scope: Covers Statistics I and Statistics II and Six Sigma Black Belt, adding such topics as control charts and statistical process control, process capability analysis, and design of experiments. As a result, this book will be useful for business professionals and industrial engineers in addition to students and professionals in the social and physical sciences. In addition, each of the 60+ concepts is covered in one or more articles. The 75 articles in the book are usually 5–7 pages long, ensuring that things are presented in “bite-sized chunks.” The first page of each article typically lists five “Keys to Understanding” which tell the reader everything they need to know on one page. This book also contains an article on “Which Statistical Tool to Use to Solve Some Common Problems”, additional “Which to Use When” articles on Control Charts, Distributions, and Charts/Graphs/Plots, as well as articles explaining how different concepts work together (e.g., how Alpha, p, Critical Value, and Test Statistic interrelate). ANDREW A. JAWLIK received his B.S. in Mathematics and his M.S. in Mathematics and Computer Science from the University of Michigan. He held jobs with IBM in marketing, sales, finance, and information technology, as well as a position as Process Executive. In these jobs, he learned how to communicate difficult technical concepts in easy - to - understand terms. He completed Lean Six Sigma Black Belt coursework at the IASSC - accredited Pyzdek Institute. In order to understand the confusing statistics involved, he wrote explanations in his own words and graphics. Using this material, he passed the certification exam with a perfect score. Those statistical explanations then became the starting point for this book. |
| null hypothesis biology example: Neutral Models in Biology Matthew H. Nitecki, Antoni Hoffman, 1987 Neutral models are constructed to help scientists understand complex patterns of form, structure, or behavior that may not be observed directly. In this unique volume, eight distinguished scientists present a comprehensive study of the use of neutral models in testing biological theories. They describe the principles of model testing and explore how they are applied to research in molecular biology, genetics, ecology, evolution, and paleontology. In addition to the editors, the contributors include Stephen Stigler, David Raup, Paul Harvey, L.B. Slobodkin, Stuart Kauffman, William Wimsatt, and James Crow. |
| null hypothesis biology example: Research Methods in Human Skeletal Biology Elizabeth A. DiGangi, Megan K. Moore, 2012-11-27 Research Methods in Human Skeletal Biology serves as the one location readers can go to not only learn how to conduct research in general, but how research is specifically conducted within human skeletal biology. It outlines the current types of research being conducted within each sub-specialty of skeletal biology, and gives the reader the tools to set up a research project in skeletal biology. It also suggests several ideas for potential projects. Each chapter has an inclusive bibliography, which can serve as a good jumpstart for project references. - Provides a step-by-step guide to conducting research in human skeletal biology - Covers diverse topics (sexing, aging, stature and ancestry estimation) and new technologies (histology, medical imaging, and geometric morphometrics) - Excellent accompaniment to existing forensic anthropology or osteology works |
| null hypothesis biology example: What If There Were No Significance Tests? Lisa L. Harlow, Stanley A. Mulaik, James H. Steiger, 2016-03-02 The classic edition of What If There Were No Significance Tests? highlights current statistical inference practices. Four areas are featured as essential for making inferences: sound judgment, meaningful research questions, relevant design, and assessing fit in multiple ways. Other options (data visualization, replication or meta-analysis), other features (mediation, moderation, multiple levels or classes), and other approaches (Bayesian analysis, simulation, data mining, qualitative inquiry) are also suggested. The Classic Edition’s new Introduction demonstrates the ongoing relevance of the topic and the charge to move away from an exclusive focus on NHST, along with new methods to help make significance testing more accessible to a wider body of researchers to improve our ability to make more accurate statistical inferences. Part 1 presents an overview of significance testing issues. The next part discusses the debate in which significance testing should be rejected or retained. The third part outlines various methods that may supplement significance testing procedures. Part 4 discusses Bayesian approaches and methods and the use of confidence intervals versus significance tests. The book concludes with philosophy of science perspectives. Rather than providing definitive prescriptions, the chapters are largely suggestive of general issues, concerns, and application guidelines. The editors allow readers to choose the best way to conduct hypothesis testing in their respective fields. For anyone doing research in the social sciences, this book is bound to become must reading. Ideal for use as a supplement for graduate courses in statistics or quantitative analysis taught in psychology, education, business, nursing, medicine, and the social sciences, the book also benefits independent researchers in the behavioral and social sciences and those who teach statistics. |
| null hypothesis biology example: Handbook of Meta-analysis in Ecology and Evolution Julia Koricheva, Jessica Gurevitch, Kerrie Mengersen, 2013-04-21 Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts. The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management. Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation Brings together experts from a broad range of fields Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species Helps you choose the right software Draws on numerous examples based on real biological datasets |
| null hypothesis biology example: The Logic of Scientific Discovery Karl Popper, 2005-11-04 Described by the philosopher A.J. Ayer as a work of 'great originality and power', this book revolutionized contemporary thinking on science and knowledge. Ideas such as the now legendary doctrine of 'falsificationism' electrified the scientific community, influencing even working scientists, as well as post-war philosophy. This astonishing work ranks alongside The Open Society and Its Enemies as one of Popper's most enduring books and contains insights and arguments that demand to be read to this day. |
| null hypothesis biology example: Principles of Statistical Inference D. R. Cox, 2006-08-10 In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses. |
| null hypothesis biology example: Systems Biology Edda Klipp, Wolfram Liebermeister, Christoph Wierling, Axel Kowald, 2016-06-27 This advanced textbook is tailored for an introductory course in Systems Biology and is well-suited for biologists as well as engineers and computer scientists. It comes with student-friendly reading lists and a companion website featuring a short exam prep version of the book and educational modeling programs. The text is written in an easily accessible style and includes numerous worked examples and study questions in each chapter. For this edition, a section on medical systems biology has been included. |
| null hypothesis biology example: Conjectures and Refutations Karl Popper, 2014-05-01 Conjectures and Refutations is one of Karl Popper's most wide-ranging and popular works, notable not only for its acute insight into the way scientific knowledge grows, but also for applying those insights to politics and to history. It provides one of the clearest and most accessible statements of the fundamental idea that guided his work: not only our knowledge, but our aims and our standards, grow through an unending process of trial and error. |
| null hypothesis biology example: Introduction to Computational Biology Bernhard Haubold, Thomas Wiehe, 2006-08-09 Written with the advanced undergraduate in mind, this book introduces into the field of Bioinformatics. The authors explain the computational and conceptional background to the analysis of large-scale sequence data. Many of the corresponding analysis methods are rooted in evolutionary thinking, which serves as a common thread throughout the book. The focus is on methods of comparative genomics and subjects covered include: alignments, gene finding, phylogeny, and the analysis of single nucleotide polymorphisms (SNPs). The volume contains exercises, questions & answers to selected problems. |
| null hypothesis biology example: Statistics with Applications in Biology and Geology Preben Blaesild, Jorgen Granfeldt, 2002-12-27 The use of statistics is fundamental to many endeavors in biology and geology. For students and professionals in these fields, there is no better way to build a statistical background than to present the concepts and techniques in a context relevant to their interests. Statistics with Applications in Biology and Geology provides a practical introduction to using fundamental parametric statistical models frequently applied to data analysis in biology and geology. Based on material developed for an introductory statistics course and classroom tested for nearly 10 years, this treatment establishes a firm basis in models, the likelihood method, and numeracy. The models addressed include one sample, two samples, one- and two-way analysis of variance, and linear regression for normal data and similar models for binomial, multinomial, and Poisson data. Building on the familiarity developed with those models, the generalized linear models are introduced, making it possible for readers to handle fairly complicated models for both continuous and discrete data. Models for directional data are treated as well. The emphasis is on parametric models, but the book also includes a chapter on the most important nonparametric tests. This presentation incorporates the use of the SAS statistical software package, which authors use to illustrate all of the statistical tools described. However, to reinforce understanding of the basic concepts, calculations for the simplest models are also worked through by hand. SAS programs and the data used in the examples and exercises are available on the Internet. |
| null hypothesis biology example: Conceptual Ecology and Invasion Biology: Reciprocal Approaches to Nature Marc W. Cadotte, Sean M. McMahon, Tadashi Fukami, 2006-07-19 The conservation threat represented by invasive species is well-known, but the scientific opportunities are underappreciated. Invasion studies have historically been largely directed at the important job of collecting case studies. Invasion biology has matured to the point of being able to incorporating itself into the heart of ecology, and should be viewed as extensions or critical experiments of ecological theory. In this edited volume, global experts in ecology and evolutionary biology explore how theories in ecology elucidate the invasion processes while also examining how specific invasions informs ecological theory. This reciprocal benefit is highlighted in a number of scales of organization: population, community and biogeographic, while employing example invaders in all major groups of organisms and from a number of regions around the globe. The chapters in this volume utilize many of the cutting edge observational, experimental, analytical and computational methods used in modern ecology. Through merging conceptual ecology and invasion biology we can obtain a better understanding of the invasion process while also developing a better understanding of how ecological systems function. |
| null hypothesis biology example: The Cambridge Dictionary of Statistics B. S. Everitt, 2006-08-17 If you use statistics and need easy access to simple, reliable definitions and explanations of modern statistical concepts, then look no further than this dictionary. Over 3600 terms are defined, covering medical, survey, theoretical, and applied statistics, including computational aspects. Entries are provided for standard and specialized statistical software. In addition, short biographies of over 100 important statisticians are given. Definitions provide enough mathematical detail to clarify concepts and give standard formulae when these are helpful. The majority of definitions then give a reference to a book or article where the user can seek further or more specialized information, and many are accompanied by graphical material to aid understanding. |
| null hypothesis biology example: A Brief History of Blood and Lymphatic Vessels Andreas Bikfalvi, 2018-04-13 This book provides a comprehensive account of vascular biology and pathology and its significance for health and disease. It systematically and chronologically explains how we came to our current understanding of the vasculature and it ́s function today, and describes in an entertaining way the diverse flaws and turns in science and medicine from the past. It thereby offers a complete and well-studied history on vascular biology and medicine. The book has an easy-to-read style and is written for students as well as scientists, physicians and lecturers in the field of biomedicine, human physiology, cardiology and hematology. |
| null hypothesis biology example: Introductory Biological Statistics John E. Havel, Raymond E. Hampton, Scott J. Meiners, 2019-04-30 A thorough understanding of biology, no matter which subfield, requires a thorough understanding of statistics. As in previous editions, Havel and Hampton (with new co-author Scott Meiners) ground students in all essential methods of descriptive and inferential statistics, using examples from different biological sciences. The authors have retained the readable, accessible writing style popular with both students and instructors. Pedagogical improvements new to this edition include concept checks in all chapters to assist students in active learning and code samples showing how to solve many of the book's examples using R. Each chapter features numerous practice and homework exercises, with larger data sets available for download at waveland.com. |
| null hypothesis biology example: Encyclopedia of Systems Biology Werner Dubitzky, Olaf Wolkenhauer, Hiroki Yokota, Kwang-Hyun Cho, 2013-06-05 Systems biology refers to the quantitative analysis of the dynamic interactions among several components of a biological system and aims to understand the behavior of the system as a whole. Systems biology involves the development and application of systems theory concepts for the study of complex biological systems through iteration over mathematical modeling, computational simulation and biological experimentation. Systems biology could be viewed as a tool to increase our understanding of biological systems, to develop more directed experiments, and to allow accurate predictions. The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology. As a result, the Encyclopedia comprises a broad range of topics relevant in the context of systems biology. The audience targeted by the Encyclopedia includes researchers, developers, teachers, students and practitioners who are interested or working in the field of systems biology. Keeping in mind the varying needs of the potential readership, we have structured and presented the content in a way that is accessible to readers from wide range of backgrounds. In contrast to encyclopedic online resources, which often rely on the general public to author their content, a key consideration in the development of the Encyclopedia of Systems Biology was to have subject matter experts define the concepts and subjects of systems biology. |
| null hypothesis biology example: Using Basic Statistics in the Behavioral and Social Sciences Annabel Ness Evans, 2013-06-06 Using Basic Statistics in the Behavioral and Social Sciences, Fifth Edition, by Annabel Ness Evans, presents introductory statistics in a practical, conceptual, and humorous way, reducing the anxiety that many students experience in introductory courses. Avoiding complex notation and derivation, the book focuses on helping readers develop an understanding of the underlying logic of statistics. Practical Focus on Research boxes engage students with realistic applications of statistics, and end-of-chapter exercises ensure student comprehension. This exciting new edition includes a greater number of realistic and engaging global examples within the social and behavioral sciences, making it ideal for use within many departments or in interdisciplinary settings. |
| null hypothesis biology example: Quantitative Research in Human Biology and Medicine Sigismund Peller, 2013-10-22 Quantitative Research in Human Biology and Medicine reflects the author's past activities and experiences in the field of medical statistics. The book presents statistical material from a variety of medical fields. The text contains chapters that deal with different aspects of vital statistics. It provides statistical surveys of perinatal mortality rate; epidemiology of various diseases, like cancer, tuberculosis, malaria, diphtheria, and scarlatina; and discussions of various aspects of human biology such as growth and development, genetics, and nutrition. The inheritance of mental qualities; the law governing multiple births; and historical demography are covered as well. Medical statisticians and physicians will find the book interesting. |
| null hypothesis biology example: DNA and Cell Biology , 2003 |
| null hypothesis biology example: Immunobiology, Human Genetics and Biostatistics Mr. Rohit Manglik, 2024-03-07 EduGorilla Publication is a trusted name in the education sector, committed to empowering learners with high-quality study materials and resources. Specializing in competitive exams and academic support, EduGorilla provides comprehensive and well-structured content tailored to meet the needs of students across various streams and levels. |
| null hypothesis biology example: R for Medicine and Biology Paul D. Lewis, 2009-05-08 R is quickly becoming the number one choice for users in the fields of biology, medicine, and bioinformatics as their main means of storing, processing, sharing, and analyzing biomedical data. R for Medicine and Biology is a step-by-step guide through the use of the statistical environment R, as used in a biomedical domain. Ideal for healthcare professionals, scientists, informaticists, and statistical experts, this resource will provide even the novice programmer with the tools necessary to process and analyze their data using the R environment. Introductory chapters guide readers in how to obtain, install, and become familiar with R and provide a clear introduction to the programming language using numerous worked examples. Later chapters outline how R can be used, not just for biomedical data analysis, but also as an environment for the processing, storing, reporting, and sharing of data and results. The remainder of the book explores areas of R application to common domains of biomedical informatics, including imaging, statistical analysis, data mining/modeling, pathology informatics, epidemiology, clinical trials, and metadata usage. R for Medicine and Biology will provide you with a single desk reference for the R environment and its many capabilities. |
| null hypothesis biology example: Palaeobiology II Derek E. G. Briggs, Peter R. Crowther, 2008-04-15 Palaeobiology: A Synthesis was widely acclaimed both for its content and production quality. Ten years on, Derek Briggs and Peter Crowther have once again brought together over 150 leading authorities from around the world to produce Palaeobiology II. Using the same successful formula, the content is arranged as a series of concise articles, taking a thematic approach to the subject, rather than treating the various fossil groups systematically. This entirely new book, with its diversity of new topics and over 100 new contributors, reflects the exciting developments in the field, including accounts of spectacular newly discovered fossils, and embraces data from other disciplines such as astrobiology, geochemistry and genetics. Palaeobiology II will be an invaluable resource, not only for palaeontologists, but also for students and researchers in other branches of the earth and life sciences. Written by an international team of recognised authorities in the field. Content is concise but informative. Demonstrates how palaeobiological studies are at the heart of a range of scientific themes. |
| null hypothesis biology example: Forensic Entomology Jason H. Byrd, Jeffery K. Tomberlin, 2019-11-27 Forensic Entomology: The Utility of Arthropods in Legal Investigations, Third Edition continues in the tradition of the two best-selling prior editions and maintains its status as the single-most comprehensive book on Forensic Entomology currently available. It includes current, in-the-field best practices contributed by top professionals in the field who have advanced it through research and fieldwork over the last several decades. The use of entomology in crime scene and forensic investigations has never been more prevalent or useful given the work that can be done with entomological evidence. The book recounts briefly the many documented historical applications of forensic entomology over several thousand years. Chapters examine the biological foundations of insect biology and scientific underpinnings of forensic entomology, the principles that govern utilizing insects in legal and criminal investigations. The field today is diverse, both in topics studied, researched and practiced, as is the field of professionals that has expanded throughout the world to become a vital forensic sub-discipline. Forensic Entomology, Third Edition celebrates this diversity by including several new chapters by premier experts in the field that covers such emerging topics as wildlife forensic entomology, microbiomes, urban forensic entomology, and larval insect identification, many of which are covered in depth for the first time. The book will be an invaluable reference for investigators, legal professionals, researchers, practicing and aspiring forensic entomologists, and for the many students enrolled in forensic science and entomology university programs. |
| null hypothesis biology example: AP® Biology Crash Course, For the New 2020 Exam, Book + Online Michael D'Alessio, 2020-02-04 REA: the test prep AP teachers recommend. |
| null hypothesis biology example: Encyclopedia of Evolutionary Biology , 2016-04-14 Encyclopedia of Evolutionary Biology, Four Volume Set is the definitive go-to reference in the field of evolutionary biology. It provides a fully comprehensive review of the field in an easy to search structure. Under the collective leadership of fifteen distinguished section editors, it is comprised of articles written by leading experts in the field, providing a full review of the current status of each topic. The articles are up-to-date and fully illustrated with in-text references that allow readers to easily access primary literature. While all entries are authoritative and valuable to those with advanced understanding of evolutionary biology, they are also intended to be accessible to both advanced undergraduate and graduate students. Broad topics include the history of evolutionary biology, population genetics, quantitative genetics; speciation, life history evolution, evolution of sex and mating systems, evolutionary biogeography, evolutionary developmental biology, molecular and genome evolution, coevolution, phylogenetic methods, microbial evolution, diversification of plants and fungi, diversification of animals, and applied evolution. Presents fully comprehensive content, allowing easy access to fundamental information and links to primary research Contains concise articles by leading experts in the field that ensures current coverage of each topic Provides ancillary learning tools like tables, illustrations, and multimedia features to assist with the comprehension process |
| null hypothesis biology example: Statistical Methods in Molecular Biology Heejung Bang, Xi Kathy Zhou, Heather L. van Epps, Madhu Mazumdar, 2016-08-23 This progressive book presents the basic principles of proper statistical analyses. It progresses to more advanced statistical methods in response to rapidly developing technologies and methodologies in the field of molecular biology. |
| null hypothesis biology example: The Cult of Statistical Significance Stephen Thomas Ziliak, Deirdre Nansen McCloskey, 2008-02-19 How the most important statistical method used in many of the sciences doesn't pass the test for basic common sense |
| null hypothesis biology example: Modern Statistics for Modern Biology SUSAN. HUBER HOLMES (WOLFGANG.), Wolfgang Huber, 2018 |
| null hypothesis biology example: BIO2010 National Research Council, Division on Earth and Life Studies, Board on Life Sciences, Committee on Undergraduate Biology Education to Prepare Research Scientists for the 21st Century, 2003-02-13 Biological sciences have been revolutionized, not only in the way research is conductedâ€with the introduction of techniques such as recombinant DNA and digital technologyâ€but also in how research findings are communicated among professionals and to the public. Yet, the undergraduate programs that train biology researchers remain much the same as they were before these fundamental changes came on the scene. This new volume provides a blueprint for bringing undergraduate biology education up to the speed of today's research fast track. It includes recommendations for teaching the next generation of life science investigators, through: Building a strong interdisciplinary curriculum that includes physical science, information technology, and mathematics. Eliminating the administrative and financial barriers to cross-departmental collaboration. Evaluating the impact of medical college admissions testing on undergraduate biology education. Creating early opportunities for independent research. Designing meaningful laboratory experiences into the curriculum. The committee presents a dozen brief case studies of exemplary programs at leading institutions and lists many resources for biology educators. This volume will be important to biology faculty, administrators, practitioners, professional societies, research and education funders, and the biotechnology industry. |
| null hypothesis biology example: Biostatistics with Python Darko Medin, 2024-11-29 Learn how to utilize biostatistics with Python for excelling in research and biomedical professions with practical exemplar projects Key Features Bridge the gap between biostatistics and life sciences with Python Work with practical exercises for real-world data analysis in biology and medicine Access a portfolio of exemplar projects in the domains of biomedicine, biotechnology, and biology Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis book leverages the author’s decade-long experience in biostatistics and data science to simplify the practical use of biostatistics with Python. The chapters show you how to clean and describe your data effectively, setting a solid foundation for accurate analysis and proficiency in biostatistical inference to help you draw meaningful conclusions from your data through hypothesis testing and effect size analysis. The book walks you through predictive modeling to harness the power of Python to create robust predictive analytics that can drive your research and professional projects forward. You'll explore clinical biostatistics, learn how to design studies, conduct survival analysis, and synthesize evidence from multiple studies with meta-analysis – skills that are crucial for making informed decisions based on comprehensive data reviews. The concluding chapters will enhance your ability to analyze biological variables, enabling you to perform detailed and accurate data analysis for biological research. This book's unique blend of biostatistics and Python helps you find practical solutions that make complex concepts easy to grasp and apply. By the end of this biostatistics book, you’ll have moved from theoretical knowledge to practical experience, allowing you to perform biostatistical analysis confidently and accurately.What you will learn Get to grips with the basics of biostatistics and Python programming Clean and describe data using Python Familiarize yourself with hypothesis testing and effect size analysis Explore predictive modeling in biostatistics Understand clinical study design and survival analysis Gain a clear understanding of the meta-analysis of clinical research data Analyze biological variables with Python Discover practical data analysis for biological research Who this book is for This book is for life science professionals, researchers, biomedical professionals, and aspiring biostatisticians who want to integrate biostatistics into their work or research. A basic understanding of life sciences, biology, or medicine is recommended to fully benefit from this book. |
| null hypothesis biology example: General Technical Report INT. , 1981 |
| null hypothesis biology example: Multiple Testing Procedures with Applications to Genomics Sandrine Dudoit, Mark J. van der Laan, 2007-12-18 This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics. |
| null hypothesis biology example: The SAGE Encyclopedia of Research Design Bruce B. Frey, 2021-12-27 The SAGE Encyclopedia of Research Design maps out how one makes decisions about research design, interprets data, and draws valid inferences, undertakes research projects in an ethical manner, and evaluates experimental design strategies and results. From A-to-Z, this four-volume work covers the spectrum of research design strategies and topics including, among other things: fundamental research design principles, ethics in the research process, quantitative versus qualitative and mixed-method designs, completely randomized designs, multiple comparison tests, diagnosing agreement between data and models, fundamental assumptions in analysis of variance, factorial treatment designs, complete and incomplete block designs, Latin square and related designs, hierarchical designs, response surface designs, split-plot designs, repeated measures designs, crossover designs, analysis of covariance, statistical software packages, and much more. Research design, with its statistical underpinnings, can be especially daunting for students and novice researchers. At its heart, research design might be described simply as a formalized approach toward problem solving, thinking, and acquiring knowledge, the success of which depends upon clearly defined objectives and appropriate choice of statistical design and analysis to meet those objectives. The SAGE Encyclopedia of Research Design will assist students and researchers with their work while providing vital information on research strategies. |
| null hypothesis biology example: @StatsbyTwitter M. D. Miles, 2012-03-09 Utilizing the Twitter convention of 140 or fewer characters, @StatsbyTwitter introduces the world of statistics through easy-to-understand, quick definitions. Together with colorful diagrams, healthcare examples, and Microsoft Excel screenshots, the reader will come to understand statistics without fear and with ease not otherwise encountered in the field. |
| null hypothesis biology example: Conservation and the Genetics of Populations Fred W. Allendorf, Gordon H. Luikart, Sally N. Aitken, 2012-12-17 Loss of biodiversity is among the greatest problems facing the world today. Conservation and the Genetics of Populations gives a comprehensive overview of the essential background, concepts, and tools needed to understand how genetic information can be used to conserve species threatened with extinction, and to manage species of ecological or commercial importance. New molecular techniques, statistical methods, and computer programs, genetic principles, and methods are becoming increasingly useful in the conservation of biological diversity. Using a balance of data and theory, coupled with basic and applied research examples, this book examines genetic and phenotypic variation in natural populations, the principles and mechanisms of evolutionary change, the interpretation of genetic data from natural populations, and how these can be applied to conservation. The book includes examples from plants, animals, and microbes in wild and captive populations. This second edition contains new chapters on Climate Change and Exploited Populations as well as new sections on genomics, genetic monitoring, emerging diseases, metagenomics, and more. One-third of the references in this edition were published after the first edition. Each of the 22 chapters and the statistical appendix have a Guest Box written by an expert in that particular topic (including James Crow, Louis Bernatchez, Loren Rieseberg, Rick Shine, and Lisette Waits). This book is essential for advanced undergraduate and graduate students of conservation genetics, natural resource management, and conservation biology, as well as professional conservation biologists working for wildlife and habitat management agencies. Additional resources for this book can be found at: www.wiley.com/go/allendorf/populations. |
| null hypothesis biology example: Excel 2010 for Biological and Life Sciences Statistics Thomas J Quirk, Meghan Quirk, Howard Horton, 2012-11-11 This is the first book to show the capabilities of Microsoft Excel to teach biological and life sciences statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical science problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2010 for Biological and Life Sciences Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned. |
| null hypothesis biology example: Excel 2010 for Environmental Sciences Statistics Thomas J. Quirk, Meghan H. Quirk, Howard F. Horton, 2015-11-29 This is the first book to show the capabilities of Microsoft Excel to teach environmental sciences statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical environmental sciences problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in environmental science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2010 for Environmental Sciences Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand environmental science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned. |
What is the difference between "is not null" and "!= null"?
Sep 21, 2021 · is not null helps the null state analyzer track nullability, != null only checks at runtime. Therefore, pattern matching can prevent null reference exceptions, particularly in …
c# - What does null! statement mean? - Stack Overflow
Feb 16, 2019 · null! is used to assign null to non-nullable variables, which is a way of promising that the variable won't be null when it is actually used. I'd use null! in a Visual Studio …
What is the difference between NULL, '\0' and 0? - Stack Overflow
A constant expression of type int with the value 0, or an expression of this type, cast to type void * is a null pointer constant, which if converted to a pointer becomes a null pointer. It is …
sql - Not equal <> != operator on NULL - Stack Overflow
Apr 14, 2011 · Which is why you can only use IS NULL/IS NOT NULL as predicates for such situations. This behavior is not specific to SQL Server. All standards-compliant SQL dialects …
SQL is null and = null - Stack Overflow
Mar 6, 2012 · The reasoning is that a null means "unknown", so the result of any comparison to a null is also "unknown". So you'll get no hit on rows by coding where my_column = null. SQL …
How do I check for null values in JavaScript? - Stack Overflow
Jan 4, 2024 · Comparing null === 0 or null > 0 or null < 0 will result in false. But as null is also an object we can detect it as a null. I made a more complex function natureof witch will do better …
How to check for an undefined or null variable in JavaScript?
The one benefit I can see is that it does not require knowing that == null and != null treat a declared variable with value undefined as equal to null. IMHO, that isn't reason enough to use …
Java null check why use == instead of .equals() - Stack Overflow
Feb 5, 2012 · @RogerWang - Good question! The answer is that null isn't an object, it's an object reference (a blank one). It's the only object reference we can write literally in code. So Object …
What is the difference between null and undefined in JavaScript?
Feb 22, 2011 · It's worth noting that while this comment was true in '11, with the advent of optional function params, emergence of type-checking systems like Flow, and pervasiveness of React …
What is the difference between "x is null" and "x == null"?
Nov 18, 2016 · As svick commented, is null calls System.Object::Equals(object, object) where == calls ceq.. IL for ==:. IL_0000: ldarg.1 // Load argument 1 onto the stack IL_0001: ldnull // Push …
What is the difference between "is not null" and "!= null"?
Sep 21, 2021 · is not null helps the null state analyzer track nullability, != null only checks at runtime. Therefore, pattern matching can prevent null reference exceptions, …
c# - What does null! statement mean? - Stack Overflow
Feb 16, 2019 · null! is used to assign null to non-nullable variables, which is a way of promising that the variable won't be null when it is actually used. I'd use null! in a Visual …
What is the difference between NULL, '\0' and 0? - Stack Overflow
A constant expression of type int with the value 0, or an expression of this type, cast to type void * is a null pointer constant, which if converted to a pointer becomes a null …
sql - Not equal <> != operator on NULL - Stack Overflow
Apr 14, 2011 · Which is why you can only use IS NULL/IS NOT NULL as predicates for such situations. This behavior is not specific to SQL Server. All standards-compliant SQL …
SQL is null and = null - Stack Overflow
Mar 6, 2012 · The reasoning is that a null means "unknown", so the result of any comparison to a null is also "unknown". So you'll get no hit on rows by coding where …
