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| bias in science examples: The Oxford Handbook of the Science of Science Communication Kathleen Hall Jamieson, Dan M. Kahan, Dietram Scheufele, 2017 On topics from genetic engineering and mad cow disease to vaccination and climate change, this Handbook draws on the insights of 57 leading science of science communication scholars who explore what social scientists know about how citizens come to understand and act on what is known by science. |
| bias in science examples: 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 |
| bias in science examples: The Bias of Science Brian Martin, 1979 |
| bias in science examples: Science Fictions Stuart Ritchie, 2021-09-16 |
| bias in science examples: 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. |
| bias in science examples: The Bias That Divides Us Keith E. Stanovich, 2021-08-31 Why we don't live in a post-truth society but rather a myside society: what science tells us about the bias that poisons our politics. In The Bias That Divides Us, psychologist Keith Stanovich argues provocatively that we don't live in a post-truth society, as has been claimed, but rather a myside society. Our problem is not that we are unable to value and respect truth and facts, but that we are unable to agree on commonly accepted truth and facts. We believe that our side knows the truth. Post-truth? That describes the other side. The inevitable result is political polarization. Stanovich shows what science can tell us about myside bias: how common it is, how to avoid it, and what purposes it serves. Stanovich explains that although myside bias is ubiquitous, it is an outlier among cognitive biases. It is unpredictable. Intelligence does not inoculate against it, and myside bias in one domain is not a good indicator of bias shown in any other domain. Stanovich argues that because of its outlier status, myside bias creates a true blind spot among the cognitive elite--those who are high in intelligence, executive functioning, or other valued psychological dispositions. They may consider themselves unbiased and purely rational in their thinking, but in fact they are just as biased as everyone else. Stanovich investigates how this bias blind spot contributes to our current ideologically polarized politics, connecting it to another recent trend: the decline of trust in university research as a disinterested arbiter. |
| bias in science examples: Communicating Science Effectively National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Committee on the Science of Science Communication: A Research Agenda, 2017-03-08 Science and technology are embedded in virtually every aspect of modern life. As a result, people face an increasing need to integrate information from science with their personal values and other considerations as they make important life decisions about medical care, the safety of foods, what to do about climate change, and many other issues. Communicating science effectively, however, is a complex task and an acquired skill. Moreover, the approaches to communicating science that will be most effective for specific audiences and circumstances are not obvious. Fortunately, there is an expanding science base from diverse disciplines that can support science communicators in making these determinations. Communicating Science Effectively offers a research agenda for science communicators and researchers seeking to apply this research and fill gaps in knowledge about how to communicate effectively about science, focusing in particular on issues that are contentious in the public sphere. To inform this research agenda, this publication identifies important influences †psychological, economic, political, social, cultural, and media-related †on how science related to such issues is understood, perceived, and used. |
| bias in science examples: The Optimism Bias Tali Sharot, 2011-06-14 Psychologists have long been aware that most people maintain an irrationally positive outlook on life—but why? Turns out, we might be hardwired that way. In this absorbing exploration, Tali Sharot—one of the most innovative neuroscientists at work today—demonstrates that optimism may be crucial to human existence. The Optimism Bias explores how the brain generates hope and what happens when it fails; how the brains of optimists and pessimists differ; why we are terrible at predicting what will make us happy; how emotions strengthen our ability to recollect; how anticipation and dread affect us; how our optimistic illusions affect our financial, professional, and emotional decisions; and more. Drawing on cutting-edge science, The Optimism Bias provides us with startling new insight into the workings of the brain and the major role that optimism plays in determining how we live our lives. |
| bias in science examples: How Social Science Got Better Matt Grossmann, 2021-07-05 It seems like most of what we read about the academic social sciences in the mainstream media is negative. The field is facing mounting criticism, as canonical studies fail to replicate, questionable research practices abound, and researcher social and political biases come under fire. In response to these criticisms, Matt Grossmann, in How Social Science Got Better, provides a robust defense of the current state of the social sciences. Applying insights from the philosophy, history, and sociology of science and providing new data on research trends and scholarly views, he argues that, far from crisis, social science is undergoing an unparalleled renaissance of ever-broader understanding and application. According to Grossmann, social science research today has never been more relevant, rigorous, or self-reflective because scholars have a much better idea of their blind spots and biases. He highlights how scholars now closely analyze the impact of racial, gender, geographic, methodological, political, and ideological differences on research questions; how the incentives of academia influence our research practices; and how universal human desires to avoid uncomfortable truths and easily solve problems affect our conclusions. Though misaligned incentive structures of course remain, a messy, collective deliberation across the research community has shifted us into an unprecedented age of theoretical diversity, open and connected data, and public scholarship. Grossmann's wide-ranging account of current trends will necessarily force the academy's many critics to rethink their lazy critiques and instead acknowledge the path-breaking advances occurring in the social sciences today. |
| bias in science examples: The Great Mental Models, Volume 1 Shane Parrish, Rhiannon Beaubien, 2024-10-15 Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage. |
| bias in science examples: Bias in Science and Communication Matthew Brian Welsh, 2018 This book is intended as an introduction to a wide variety of biases affecting human cognition, with a specific focus on how they affect scientists and the communication of science. The role of this book is to lay out how these common biases affect the specific types of judgements, decisions and communications made by scientists. |
| bias in science examples: Key Topics in Surgical Research and Methodology Thanos Athanasiou, H. Debas, Ara Darzi, 2010-02-28 Key Topics in Surgical Research and Methodology represents a comprehensive reference text accessible to the surgeon embarking on an academic career. Key themes emphasize and summarize the text. Four key elements are covered, i.e. Surgical Research, Research Methodology, Practical Problems and Solutions on Research as well as Recent Developments and Future Prospects in Surgical Research and Practice. |
| bias in science examples: Basics of Software Engineering Experimentation Natalia Juristo, Ana M. Moreno, 2013-03-14 Basics of Software Engineering Experimentation is a practical guide to experimentation in a field which has long been underpinned by suppositions, assumptions, speculations and beliefs. It demonstrates to software engineers how Experimental Design and Analysis can be used to validate their beliefs and ideas. The book does not assume its readers have an in-depth knowledge of mathematics, specifying the conceptual essence of the techniques to use in the design and analysis of experiments and keeping the mathematical calculations clear and simple. Basics of Software Engineering Experimentation is practically oriented and is specially written for software engineers, all the examples being based on real and fictitious software engineering experiments. |
| bias in science examples: Sexual Selection Regina H. Macedo, Glauco Machado, 2013-09-25 Sexual Selection: Perspectives and Models from the Neotropics presents new sexual selection research based upon neotropical species. As neotropical regions are destroyed at an alarming rate, with an estimated 140 species of rainforest plants and animals going extinct every day, it is important to bring neotropical research to the fore now. Sexual selection occurs when the male or female of a species is attracted by certain characteristics such as form, color or behavior. When those features lead to a greater probability of successful mating, they become more prominent in the species. Although most theoretical concepts concerning sexual selection and reproductive strategies are based upon North American and European fauna, the Neotropical region encompasses much more biodiversity, with as many as 15,000 plant and animal species in a single acre of rain forest. This book illustrates concepts in sexual selection through themes ranging from female cryptic choice in insects, sexual conflict in fish, interaction between sexual selection and the immune system, nuptial gifts, visual and acoustic sexual signaling, parental investment, to alternative mating strategies, among others. These approaches distinguish Sexual Selection from current publications in sexual selection, mainly because of the latitudinal and taxonomic focus, so that readers will be introduced to systems mostly unknown outside the tropics, several of which bring into question some well-established patterns for temperate regions. - Synthesizes sexual selection research on species from the Neotropics - Combines different perspectives and levels of analysis using a broad taxonomic basis, introducing readers to systems mostly unknown outside the tropics and bringing into question well-established patterns for temperate regions - Includes contributions exploring concepts and theory as well as discussions on a variety of Neotropical vertebrates and invertebrates, such as insects, fish, arthropods and birds |
| bias in science examples: Applying Quantitative Bias Analysis to Epidemiologic Data Timothy L. Lash, Matthew P. Fox, Aliza K. Fink, 2011-04-14 Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow. |
| bias in science examples: Social Science Research Anol Bhattacherjee, 2012-04-01 This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages. |
| bias in science examples: Biased Jennifer L. Eberhardt, PhD, 2019-03-26 Poignant....important and illuminating.—The New York Times Book Review Groundbreaking.—Bryan Stevenson, New York Times bestselling author of Just Mercy From one of the world’s leading experts on unconscious racial bias come stories, science, and strategies to address one of the central controversies of our time How do we talk about bias? How do we address racial disparities and inequities? What role do our institutions play in creating, maintaining, and magnifying those inequities? What role do we play? With a perspective that is at once scientific, investigative, and informed by personal experience, Dr. Jennifer Eberhardt offers us the language and courage we need to face one of the biggest and most troubling issues of our time. She exposes racial bias at all levels of society—in our neighborhoods, schools, workplaces, and criminal justice system. Yet she also offers us tools to address it. Eberhardt shows us how we can be vulnerable to bias but not doomed to live under its grip. Racial bias is a problem that we all have a role to play in solving. |
| bias in science examples: Good Research Practice in Non-Clinical Pharmacology and Biomedicine Anton Bespalov, Martin C. Michel, Thomas Steckler, 2020-01-01 This open access book, published under a CC BY 4.0 license in the Pubmed indexed book series Handbook of Experimental Pharmacology, provides up-to-date information on best practice to improve experimental design and quality of research in non-clinical pharmacology and biomedicine. |
| bias in science examples: Scientific Research in Education National Research Council, Division of Behavioral and Social Sciences and Education, Center for Education, Committee on Scientific Principles for Education Research, 2002-03-28 Researchers, historians, and philosophers of science have debated the nature of scientific research in education for more than 100 years. Recent enthusiasm for evidence-based policy and practice in educationâ€now codified in the federal law that authorizes the bulk of elementary and secondary education programsâ€have brought a new sense of urgency to understanding the ways in which the basic tenets of science manifest in the study of teaching, learning, and schooling. Scientific Research in Education describes the similarities and differences between scientific inquiry in education and scientific inquiry in other fields and disciplines and provides a number of examples to illustrate these ideas. Its main argument is that all scientific endeavors share a common set of principles, and that each fieldâ€including education researchâ€develops a specialization that accounts for the particulars of what is being studied. The book also provides suggestions for how the federal government can best support high-quality scientific research in education. |
| bias in science examples: Fostering Integrity in Research National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Committee on Responsible Science, 2018-01-13 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 †or distort †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. |
| bias in science examples: Saving Women's Lives National Research Council, Institute of Medicine, Policy and Global Affairs, Board on Science, Technology, and Economic Policy, National Cancer Policy Board, Committee on New Approaches to Early Detection and Diagnosis of Breast Cancer, 2005-03-18 The outlook for women with breast cancer has improved in recent years. Due to the combination of improved treatments and the benefits of mammography screening, breast cancer mortality has decreased steadily since 1989. Yet breast cancer remains a major problem, second only to lung cancer as a leading cause of death from cancer for women. To date, no means to prevent breast cancer has been discovered and experience has shown that treatments are most effective when a cancer is detected early, before it has spread to other tissues. These two facts suggest that the most effective way to continue reducing the death toll from breast cancer is improved early detection and diagnosis. Building on the 2001 report Mammography and Beyond, this new book not only examines ways to improve implementation and use of new and current breast cancer detection technologies but also evaluates the need to develop tools that identify women who would benefit most from early detection screening. Saving Women's Lives: Strategies for Improving Breast Cancer Detection and Diagnosis encourages more research that integrates the development, validation, and analysis of the types of technologies in clinical practice that promote improved risk identification techniques. In this way, methods and technologies that improve detection and diagnosis can be more effectively developed and implemented. |
| bias in science examples: Strengthening Forensic Science in the United States National Research Council, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Policy and Global Affairs, Committee on Science, Technology, and Law, Committee on Identifying the Needs of the Forensic Sciences Community, 2009-07-29 Scores of talented and dedicated people serve the forensic science community, performing vitally important work. However, they are often constrained by lack of adequate resources, sound policies, and national support. It is clear that change and advancements, both systematic and scientific, are needed in a number of forensic science disciplines to ensure the reliability of work, establish enforceable standards, and promote best practices with consistent application. Strengthening Forensic Science in the United States: A Path Forward provides a detailed plan for addressing these needs and suggests the creation of a new government entity, the National Institute of Forensic Science, to establish and enforce standards within the forensic science community. The benefits of improving and regulating the forensic science disciplines are clear: assisting law enforcement officials, enhancing homeland security, and reducing the risk of wrongful conviction and exoneration. Strengthening Forensic Science in the United States gives a full account of what is needed to advance the forensic science disciplines, including upgrading of systems and organizational structures, better training, widespread adoption of uniform and enforceable best practices, and mandatory certification and accreditation programs. While this book provides an essential call-to-action for congress and policy makers, it also serves as a vital tool for law enforcement agencies, criminal prosecutors and attorneys, and forensic science educators. |
| bias in science examples: Nonresponse in Social Science Surveys National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on a Research Agenda for the Future of Social Science Data Collection, 2013-10-26 For many household surveys in the United States, responses rates have been steadily declining for at least the past two decades. A similar decline in survey response can be observed in all wealthy countries. Efforts to raise response rates have used such strategies as monetary incentives or repeated attempts to contact sample members and obtain completed interviews, but these strategies increase the costs of surveys. This review addresses the core issues regarding survey nonresponse. It considers why response rates are declining and what that means for the accuracy of survey results. These trends are of particular concern for the social science community, which is heavily invested in obtaining information from household surveys. The evidence to date makes it apparent that current trends in nonresponse, if not arrested, threaten to undermine the potential of household surveys to elicit information that assists in understanding social and economic issues. The trends also threaten to weaken the validity of inferences drawn from estimates based on those surveys. High nonresponse rates create the potential or risk for bias in estimates and affect survey design, data collection, estimation, and analysis. The survey community is painfully aware of these trends and has responded aggressively to these threats. The interview modes employed by surveys in the public and private sectors have proliferated as new technologies and methods have emerged and matured. To the traditional trio of mail, telephone, and face-to-face surveys have been added interactive voice response (IVR), audio computer-assisted self-interviewing (ACASI), web surveys, and a number of hybrid methods. Similarly, a growing research agenda has emerged in the past decade or so focused on seeking solutions to various aspects of the problem of survey nonresponse; the potential solutions that have been considered range from better training and deployment of interviewers to more use of incentives, better use of the information collected in the data collection, and increased use of auxiliary information from other sources in survey design and data collection. Nonresponse in Social Science Surveys: A Research Agenda also documents the increased use of information collected in the survey process in nonresponse adjustment. |
| bias in science examples: The Demon-Haunted World Carl Sagan, 2011-07-06 A prescient warning of a future we now inhabit, where fake news stories and Internet conspiracy theories play to a disaffected American populace “A glorious book . . . A spirited defense of science . . . From the first page to the last, this book is a manifesto for clear thought.”—Los Angeles Times How can we make intelligent decisions about our increasingly technology-driven lives if we don’t understand the difference between the myths of pseudoscience and the testable hypotheses of science? Pulitzer Prize-winning author and distinguished astronomer Carl Sagan argues that scientific thinking is critical not only to the pursuit of truth but to the very well-being of our democratic institutions. Casting a wide net through history and culture, Sagan examines and authoritatively debunks such celebrated fallacies of the past as witchcraft, faith healing, demons, and UFOs. And yet, disturbingly, in today's so-called information age, pseudoscience is burgeoning with stories of alien abduction, channeling past lives, and communal hallucinations commanding growing attention and respect. As Sagan demonstrates with lucid eloquence, the siren song of unreason is not just a cultural wrong turn but a dangerous plunge into darkness that threatens our most basic freedoms. Praise for The Demon-Haunted World “Powerful . . . A stirring defense of informed rationality. . . Rich in surprising information and beautiful writing.”—The Washington Post Book World “Compelling.”—USA Today “A clear vision of what good science means and why it makes a difference. . . . A testimonial to the power of science and a warning of the dangers of unrestrained credulity.”—The Sciences “Passionate.”—San Francisco Examiner-Chronicle |
| bias in science examples: Anti-Bias Education for Young Children and Ourselves Louise Derman-Sparks, Julie Olsen Edwards, 2020-04-07 Anti-bias education begins with you! Become a skilled anti-bias teacher with this practical guidance to confronting and eliminating barriers. |
| bias in science examples: Foundations of Epidemiology Marit L. Bovbjerg, 2020-10 Foundations of Epidemiology is an open access, introductory epidemiology text intended for students and practitioners in public or allied health fields. It covers epidemiologic thinking, causality, incidence and prevalence, public health surveillance, epidemiologic study designs and why we care about which one is used, measures of association, random error and bias, confounding and effect modification, and screening. Concepts are illustrated with numerous examples drawn from contemporary and historical public health issues. |
| bias in science examples: Gender Shrapnel in the Academic Workplace Ellen Mayock, 2016-05-27 This book employs the image of “shrapnel,” bits of scattered metal that can hit purposeful targets or unwitting bystanders, to narrate the story of workplace power and gender discrimination. The project interweaves stories of gender shrapnel with an examination of national rhetoric surrounding business, education, and law to uncover underlying phenomena that contribute to discourse on privilege and gender in the academic workplace. Using concrete examples that serve as case studies for subsequent discussion of data about women in the workforce, language use and misuse, sexual harassment, silence and shutting up, and hiring, training, promotion, and the glass ceiling, Mayock explores the deeper implications of gender inequity in the workplace. |
| bias in science examples: Judgment Under Uncertainty Daniel Kahneman, Paul Slovic, Amos Tversky, 1982-04-30 Thirty-five chapters describe various judgmental heuristics and the biases they produce, not only in laboratory experiments, but in important social, medical, and political situations as well. Most review multiple studies or entire subareas rather than describing single experimental studies. |
| bias in science examples: Noise Daniel Kahneman, Olivier Sibony, Cass R. Sunstein, 2021-05-18 From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones—a tour de force” (New York Times). Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical. In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions. Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it. |
| bias in science examples: Laziness Does Not Exist Devon Price, 2021-01-05 From social psychologist Dr. Devon Price, a conversational, stirring call to “a better, more human way to live” (Cal Newport, New York Times bestselling author) that examines the “laziness lie”—which falsely tells us we are not working or learning hard enough. Extra-curricular activities. Honors classes. 60-hour work weeks. Side hustles. Like many Americans, Dr. Devon Price believed that productivity was the best way to measure self-worth. Price was an overachiever from the start, graduating from both college and graduate school early, but that success came at a cost. After Price was diagnosed with a severe case of anemia and heart complications from overexertion, they were forced to examine the darker side of all this productivity. Laziness Does Not Exist explores the psychological underpinnings of the “laziness lie,” including its origins from the Puritans and how it has continued to proliferate as digital work tools have blurred the boundaries between work and life. Using in-depth research, Price explains that people today do far more work than nearly any other humans in history yet most of us often still feel we are not doing enough. Filled with practical and accessible advice for overcoming society’s pressure to do more, and featuring interviews with researchers, consultants, and experiences from real people drowning in too much work, Laziness Does Not Exist “is the book we all need right now” (Caroline Dooner, author of The F*ck It Diet). |
| bias in science examples: The Madame Curie Complex Julie Des Jardins, 2010-03-01 The historian and author of Lillian Gilbreth examines the “Great Man” myth of science with profiles of women scientists from Marie Curie to Jane Goodall. Why is science still considered to be predominantly male profession? In The Madame Curie Complex, Julie Des Jardin dismantles the myth of the lone male genius, reframing the history of science with revelations about women’s substantial contributions to the field. She explores the lives of some of the most famous female scientists, including Jane Goodall, the eminent primatologist; Rosalind Franklin, the chemist whose work anticipated the discovery of DNA’s structure; Rosalyn Yalow, the Nobel Prize-winning physicist; and, of course, Marie Curie, the Nobel Prize-winning pioneer whose towering, mythical status has both empowered and stigmatized future generations of women considering a life in science. With lively anecdotes and vivid detail, The Madame Curie Complex reveals how women scientists have changed the course of science—and the role of the scientist—throughout the twentieth century. They often asked different questions, used different methods, and came up with different, groundbreaking explanations for phenomena in the natural world. |
| bias in science examples: Algorithms of Oppression Safiya Umoja Noble, 2018-02-20 Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author |
| bias in science examples: Unfair Adam Benforado, 2015 A legal scholar exposes the psychological forces that undermine the American criminal justice system, arguing that unless hidden biases are addressed, social inequality will widen, and proposes reforms to prevent injustice and help achieve true equality before the law. |
| bias in science examples: Cochrane Handbook for Systematic Reviews of Interventions Julian P. T. Higgins, Sally Green, 2008-11-24 Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves. |
| bias in science examples: Blindspot Mahzarin R. Banaji, Anthony G. Greenwald, 2016-08-16 “Accessible and authoritative . . . While we may not have much power to eradicate our own prejudices, we can counteract them. The first step is to turn a hidden bias into a visible one. . . . What if we’re not the magnanimous people we think we are?”—The Washington Post I know my own mind. I am able to assess others in a fair and accurate way. These self-perceptions are challenged by leading psychologists Mahzarin R. Banaji and Anthony G. Greenwald as they explore the hidden biases we all carry from a lifetime of exposure to cultural attitudes about age, gender, race, ethnicity, religion, social class, sexuality, disability status, and nationality. “Blindspot” is the authors’ metaphor for the portion of the mind that houses hidden biases. Writing with simplicity and verve, Banaji and Greenwald question the extent to which our perceptions of social groups—without our awareness or conscious control—shape our likes and dislikes and our judgments about people’s character, abilities, and potential. In Blindspot, the authors reveal hidden biases based on their experience with the Implicit Association Test, a method that has revolutionized the way scientists learn about the human mind and that gives us a glimpse into what lies within the metaphoric blindspot. The title’s “good people” are those of us who strive to align our behavior with our intentions. The aim of Blindspot is to explain the science in plain enough language to help well-intentioned people achieve that alignment. By gaining awareness, we can adapt beliefs and behavior and “outsmart the machine” in our heads so we can be fairer to those around us. Venturing into this book is an invitation to understand our own minds. Brilliant, authoritative, and utterly accessible, Blindspot is a book that will challenge and change readers for years to come. Praise for Blindspot “Conversational . . . easy to read, and best of all, it has the potential, at least, to change the way you think about yourself.”—Leonard Mlodinow, The New York Review of Books “Banaji and Greenwald deserve a major award for writing such a lively and engaging book that conveys an important message: Mental processes that we are not aware of can affect what we think and what we do. Blindspot is one of the most illuminating books ever written on this topic.”—Elizabeth F. Loftus, Ph.D., distinguished professor, University of California, Irvine; past president, Association for Psychological Science; author of Eyewitness Testimony |
| bias in science examples: Cohort Studies in Health Sciences R. Mauricio Barría, 2018 Introductory Chapter: The Contribution of Cohort Studies to Health Sciences. |
| bias in science examples: Persistent Forecasting of Disruptive Technologies National Research Council, Division on Engineering and Physical Sciences, Committee on Forecasting Future Disruptive Technologies, 2010-02-15 Technological innovations are key causal agents of surprise and disruption. In the recent past, the United States military has encountered unexpected challenges in the battlefield due in part to the adversary's incorporation of technologies not traditionally associated with weaponry. Recognizing the need to broaden the scope of current technology forecasting efforts, the Office of the Director, Defense Research and Engineering (DDR&E) and the Defense Intelligence Agency (DIA) tasked the Committee for Forecasting Future Disruptive Technologies with providing guidance and insight on how to build a persistent forecasting system to predict, analyze, and reduce the impact of the most dramatically disruptive technologies. The first of two reports, this volume analyzes existing forecasting methods and processes. It then outlines the necessary characteristics of a comprehensive forecasting system that integrates data from diverse sources to identify potentially game-changing technological innovations and facilitates informed decision making by policymakers. The committee's goal was to help the reader understand current forecasting methodologies, the nature of disruptive technologies and the characteristics of a persistent forecasting system for disruptive technology. Persistent Forecasting of Disruptive Technologies is a useful text for the Department of Defense, Homeland Security, the Intelligence community and other defense agencies across the nation. |
| bias in science examples: Responsible Science Committee on Science, Engineering, and Public Policy (U.S.). Panel on Scientific Responsibility and the Conduct of Research, 1992 Responsible Science is a comprehensive review of factors that influence the integrity of the research process. Volume I examines reports on the incidence of misconduct in science and reviews institutional and governmental efforts to handle cases of misconduct. The result of a two-year study by a panel of experts convened by the National Academy of Sciences, this book critically analyzes the impact of today's research environment on the traditional checks and balances that foster integrity in science. Responsible Science is a provocative examination of the role of educational efforts; research guidelines; and the contributions of individual scientists, mentors, and institutional officials in encouraging responsible research practices. |
| bias in science examples: The Bad Food Bible Aaron E. Carroll, 2017 Reveals the positive benefits of enjoying moderate portions of vilified ingredients ranging from red meat and alcohol to gluten and salt. |
| bias in science examples: Rigor Mortis Richard Harris, 2017-04-04 An essential book to understanding whether the new miracle cure is good science or simply too good to be true American taxpayers spend $30 billion annually funding biomedical research, but over half of these studies can't be replicated due to poor experimental design, improper methods, and sloppy statistics. Bad science doesn't just hold back medical progress, it can sign the equivalent of a death sentence for terminal patients. In Rigor Mortis, Richard Harris explores these urgent issues with vivid anecdotes, personal stories, and interviews with the top biomedical researchers. We need to fix our dysfunctional biomedical system -- before it's too late. |
机器学习中的 Bias（偏差）、Error（误差）、Variance（方差） …
首先明确一点，Bias和Variance是针对Generalization（一般化，泛化）来说的。. 在机器学习中，我们用训练数据集去训练（学习）一个model（模型），通常的做法是定义一个Loss …
神经网络中的偏置（bias）究竟有什么用？ - 知乎
神经网络中的偏置（bias）究竟有什么用？ 最近写了一下模式识别的作业，简单的用python实现了一个三层神经网络，发现不加偏置的话，网络的训练精度一直不能够提升，加了偏执之后反而 …
偏差——bias与deviation的联系/区别？ - 知乎
各位同学，你们有没有想过‘偏见’在英语中是怎么说的？没错，答案就是'bias'！而且，我们这次还结合了一款超酷的桌面背单词软件，让你在学习单词的同时，也能感受到科技的魅
英文中prejudice和bias的区别? - 知乎
Bias：Bias is a tendency to prefer one person or thing to another, and to favour that person or thing. 可见 bias 所表示的意思是“偏爱”，其本质是一种喜好，而非厌恶，所以没有偏见的意思。
sci投稿Declaration of interest怎么写? - 知乎
正在写SCI的小伙伴看到这篇回答有福了！作为一个在硕士阶段发表了4篇SCI（一区×2，二区×2）的人，本回答就好好给你唠唠究竟该如何撰写Declaration of interest利益声明部分。
确认偏误是什么？如何系统地克服确认偏误？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
Linear classifier 里的 bias 有什么用？ - 知乎
Oct 27, 2015 · 你想象一下一维的情况，如果有两个点 -1 是负类， -2 是正类。如果没有bias，你的分类边界只能是过远点的一条垂直线，没法区分出这两个类别，bias给你提供了在特征空间上 …
选择性偏差（selection bias）指的是什么？ - 知乎
选择性偏差指的是在研究过程中因样本选择的非随机性而导致得到的结论存在偏差，包括自选择偏差（self-selection bias）和样本选择偏差（sample-selection bias）。消除选择性偏差，我们 …
哪里有标准的机器学习术语(翻译)对照表？ - 知乎
预测偏差 (prediction bias) 一种值，用于表明预测平均值与数据集中标签的平均值相差有多大。 预训练模型 (pre-trained model) 已经过训练的模型或模型组件（例如嵌套）。有时，您需要将预 …
如何理解Adam算法(Adaptive Moment Estimation)？ - 知乎
完整的Adam更新算法也包含了一个偏置（bias）矫正机制，因为m,v两个矩阵初始为0，在没有完全热身之前存在偏差，需要采取一些补偿措施。 不同最优化方法效果
机器学习中的 Bias（偏差）、Error（误差）、Variance（方差）有 …
首先明确一点，Bias和Variance是针对Generalization（一般化，泛化）来说的。. 在机器学习中，我们用训练数据集去训练（学习）一个model（模型），通常的做法是定义一个Loss …
神经网络中的偏置（bias）究竟有什么用？ - 知乎
神经网络中的偏置（bias）究竟有什么用？ 最近写了一下模式识别的作业，简单的用python实现了一个三层神经网络，发现不加偏置的话，网络的训练精度一直不能够提升，加了偏执之后反而 …
偏差——bias与deviation的联系/区别？ - 知乎
各位同学，你们有没有想过‘偏见’在英语中是怎么说的？没错，答案就是'bias'！而且，我们这次还结合了一款超酷的桌面背单词软件，让你在学习单词的同时，也能感受到科技的魅
英文中prejudice和bias的区别? - 知乎
Bias：Bias is a tendency to prefer one person or thing to another, and to favour that person or thing. 可见 bias 所表示的意思是“偏爱”，其本质是一种喜好，而非厌恶，所以没有偏见的意思。
sci投稿Declaration of interest怎么写? - 知乎
正在写SCI的小伙伴看到这篇回答有福了！作为一个在硕士阶段发表了4篇SCI（一区×2，二区×2）的人，本回答就好好给你唠唠究竟该如何撰写Declaration of interest利益声明部分。
确认偏误是什么？如何系统地克服确认偏误？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
Linear classifier 里的 bias 有什么用？ - 知乎
Oct 27, 2015 · 你想象一下一维的情况，如果有两个点 -1 是负类， -2 是正类。如果没有bias，你的分类边界只能是过远点的一条垂直线，没法区分出这两个类别，bias给你提供了在特征空间上 …
选择性偏差（selection bias）指的是什么？ - 知乎
选择性偏差指的是在研究过程中因样本选择的非随机性而导致得到的结论存在偏差，包括自选择偏差（self-selection bias）和样本选择偏差（sample-selection bias）。消除选择性偏差，我们 …
哪里有标准的机器学习术语(翻译)对照表？ - 知乎
预测偏差 (prediction bias) 一种值，用于表明预测平均值与数据集中标签的平均值相差有多大。 预训练模型 (pre-trained model) 已经过训练的模型或模型组件（例如嵌套）。有时，您需要将预 …
如何理解Adam算法(Adaptive Moment Estimation)？ - 知乎
完整的Adam更新算法也包含了一个偏置（bias）矫正机制，因为m,v两个矩阵初始为0，在没有完全热身之前存在偏差，需要采取一些补偿措施。 不同最优化方法效果
