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Author: Marcel Boumans Publisher: Routledge ISBN: 1317319486 Category : History Languages : en Pages : 264
Book Description
Assessment of error and uncertainty is a vital component of both natural and social science. This edited volume presents case studies of research practices across a wide spectrum of scientific fields. It compares methodologies and presents the ingredients needed for an overarching framework applicable to all.
Author: Marcel Boumans Publisher: Routledge ISBN: 1317319486 Category : History Languages : en Pages : 264
Book Description
Assessment of error and uncertainty is a vital component of both natural and social science. This edited volume presents case studies of research practices across a wide spectrum of scientific fields. It compares methodologies and presents the ingredients needed for an overarching framework applicable to all.
Author: National Academy of Engineering Publisher: National Academies Press ISBN: 9780309051965 Category : Social Science Languages : en Pages : 44
Book Description
Since the first edition of On Being a Scientist was published in 1989, more than 200,000 copies have been distributed to graduate and undergraduate science students. Now this well-received booklet has been updated to incorporate the important developments in science ethics of the past 6 years and includes updated examples and material from the landmark volume Responsible Science (National Academy Press, 1992). The revision reflects feedback from readers of the original version. In response to graduate students' requests, it offers several case studies in science ethics that pose provocative and realistic scenarios of ethical dilemmas and issues. On Being a Scientist presents penetrating discussions of the social and historical context of science, the allocation of credit for discovery, the scientist's role in society, the issues revolving around publication, and many other aspects of scientific work. The booklet explores the inevitable conflicts that arise when the black and white areas of science meet the gray areas of human values and biases. Written in a conversational style, this booklet will be of great interest to students entering scientific research, their instructors and mentors, and anyone interested in the role of scientific discovery in society.
Author: National Academies of Sciences, Engineering, and Medicine Publisher: National Academies Press ISBN: 0309391253 Category : Science Languages : en Pages : 327
Book Description
The integrity of knowledge that emerges from research is based on individual and collective adherence to core values of objectivity, honesty, openness, fairness, accountability, and stewardship. Integrity in science means that the organizations in which research is conducted encourage those involved to exemplify these values in every step of the research process. Understanding the dynamics that support â€" or distort â€" practices that uphold the integrity of research by all participants ensures that the research enterprise advances knowledge. The 1992 report Responsible Science: Ensuring the Integrity of the Research Process evaluated issues related to scientific responsibility and the conduct of research. It provided a valuable service in describing and analyzing a very complicated set of issues, and has served as a crucial basis for thinking about research integrity for more than two decades. However, as experience has accumulated with various forms of research misconduct, detrimental research practices, and other forms of misconduct, as subsequent empirical research has revealed more about the nature of scientific misconduct, and because technological and social changes have altered the environment in which science is conducted, it is clear that the framework established more than two decades ago needs to be updated. Responsible Science served as a valuable benchmark to set the context for this most recent analysis and to help guide the committee's thought process. Fostering Integrity in Research identifies best practices in research and recommends practical options for discouraging and addressing research misconduct and detrimental research practices.
Author: Alex Reinhart Publisher: No Starch Press ISBN: 1593276206 Category : Mathematics Languages : en Pages : 177
Book Description
Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics. You'll find advice on: –Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan –How to think about p values, significance, insignificance, confidence intervals, and regression –Choosing the right sample size and avoiding false positives –Reporting your analysis and publishing your data and source code –Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know. The first step toward statistics done right is Statistics Done Wrong.
Author: Paul P. Biemer Publisher: John Wiley & Sons ISBN: 1119041678 Category : Social Science Languages : en Pages : 624
Book Description
Featuring a timely presentation of total survey error (TSE), this edited volume introduces valuable tools for understanding and improving survey data quality in the context of evolving large-scale data sets This book provides an overview of the TSE framework and current TSE research as related to survey design, data collection, estimation, and analysis. It recognizes that survey data affects many public policy and business decisions and thus focuses on the framework for understanding and improving survey data quality. The book also addresses issues with data quality in official statistics and in social, opinion, and market research as these fields continue to evolve, leading to larger and messier data sets. This perspective challenges survey organizations to find ways to collect and process data more efficiently without sacrificing quality. The volume consists of the most up-to-date research and reporting from over 70 contributors representing the best academics and researchers from a range of fields. The chapters are broken out into five main sections: The Concept of TSE and the TSE Paradigm, Implications for Survey Design, Data Collection and Data Processing Applications, Evaluation and Improvement, and Estimation and Analysis. Each chapter introduces and examines multiple error sources, such as sampling error, measurement error, and nonresponse error, which often offer the greatest risks to data quality, while also encouraging readers not to lose sight of the less commonly studied error sources, such as coverage error, processing error, and specification error. The book also notes the relationships between errors and the ways in which efforts to reduce one type can increase another, resulting in an estimate with larger total error. This book: • Features various error sources, and the complex relationships between them, in 25 high-quality chapters on the most up-to-date research in the field of TSE • Provides comprehensive reviews of the literature on error sources as well as data collection approaches and estimation methods to reduce their effects • Presents examples of recent international events that demonstrate the effects of data error, the importance of survey data quality, and the real-world issues that arise from these errors • Spans the four pillars of the total survey error paradigm (design, data collection, evaluation and analysis) to address key data quality issues in official statistics and survey research Total Survey Error in Practice is a reference for survey researchers and data scientists in research areas that include social science, public opinion, public policy, and business. It can also be used as a textbook or supplementary material for a graduate-level course in survey research methods.
Author: National Academies of Sciences, Engineering, and Medicine Publisher: National Academies Press ISBN: 0309486165 Category : Science Languages : en Pages : 257
Book Description
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.
Author: Aaron Santiago Chavez Publisher: ISBN: Category : Errors Languages : en Pages : 68
Book Description
This paper addresses the problem of accurately characterizing error under faulty material conditions. I argue that where there is an experimental setup whose reliability is gauged by measurement robustness and is simultaneously undermined by unbeknownst systematic material error, any inferences about the measurement outcomes of that particular setup are warranted. The only way to discount a measurement result as erroneous is to test its fit within a broader theoretical framework. Thus, it is not until theory is well-developed that we have epistemic justification for claiming that such a result is in error. In the following I discuss three cases in the history of science where measurement robustness is the operative metric for reliability, but where the results are products of error, thus leading to three different kinds of inferences in relation to theory: discovery of a new phenomenon against well-established theory, discovery of a new phenomenon against an underdeveloped theory, and discovery of a new phenomenon compatible or embeddable with existing theory.
Author: Marcel Boumans
Author: Marcel Boumans
Author: National Academy of Engineering
Author: National Academies of Sciences, Engineering, and Medicine
Author: Deborah G. Mayo
Author: Alex Reinhart
Author: Paul P. Biemer
Author: Stuart Ritchie
Author: National Academies of Sciences, Engineering, and Medicine
Author: Aaron Santiago Chavez
Author: Susan B. Rubin