Isbn: 9783030990909 - fundamentals of statistical inference: what is the meaning of random error? (springerbriefs in applied statistics and econometrics) (24 resultados)

Soft cover. Condición: Very Good. 1st Edition. Content Description. Published by Springer in 2022 as part of the SpringerBriefs in Applied Statistics and Econometrics series.The authors provide an accessible examination of the foundations of statistical inference and the role of random error in drawing conclusions from sample data. Topics include random sampling, sampling distributions, effect-size estimation, estimation uncertainty, p-values, null-hypothesis significance testing, inferential errors, and statistical inference in experiments. The book also addresses contemporary concerns about statistical significance, replication, and efforts to improve statistical inference beyond conventional p < .05 decision rules. Intended for graduate students, researchers, and statistical practitioners with a basic knowledge of statistics, particularly readers interested in the current debate over statistical reform and the appropriate interpretation of statistical evidence. Condition Description. Very Good. Clean, apparently unread copy with exterior shelf and storage wear. Covers show light rubbing and minor edge and corner wear, with some curling/waviness, particularly along the fore-edge. Spine and binding are sound. Page block is clean. Interior pages are clean and bright; no writing, underlining, or highlighting observed on inspection. Please see photographs for the book's exterior condition.…

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Paperback. Condición: New. 1st ed. 2022. This book provides a coherent description of foundational matters concerning statistical inference and shows how statistics can help us make inductive inferences about a broader context, based only on a limited dataset such as a random sample drawn from a larger population. By relating those basics to the methodological debate about inferential errors associated with p-values and statistical significance testing, readers are provided with a clear grasp of what statistical inference presupposes, and what it can and cannot do. To facilitate intuition, the representations throughout the book are as non-technical as possible.The central inspiration behind the text comes from the scientific debate about good statistical practices and the replication crisis. Calls for statistical reform include an unprecedented methodological warning from the American Statistical Association in 2016, a special issue "Statistical Inference in the 21st Century:A World Beyond p The American Statistician in 2019, and a widely supported call to "Retire statistical significance" in Nature in 2019.The book elucidates the probabilistic foundations and the potential of sample-based inferences, including random data generation, effect size estimation, and the assessment of estimation uncertainty caused by random error. Based on a thorough understanding of those basics, it then describes the p-value concept and the null-hypothesis-significance-testing ritual, and finally points out the ensuing inferential errors. This provides readers with the competence to avoid ill-guided statistical routines and misinterpretations of statistical quantities in the future.Intended for readers with an interest in understanding the role of statistical inference, the book provides a prudent assessment of the knowledge gain that can be obtained from a particular setof data under consideration of the uncertainty caused by random error. More particularly, it offers an accessible resource for graduate students as well as statistical practitioners who have a basic knowledge of statistics. Last but not least, it is aimed at scientists with a genuine methodological interest in the above-mentioned reform debate.…

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Paperback. Condición: Brand New. 147 pages. 9.25x6.10x0.32 inches. In Stock.

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a coherent description of foundational matters concerning statistical inference and shows how statistics can help us make inductive inferences about a broader context, based only on a limited dataset such as a random sample drawn from a larger population. By relating those basics to the methodological debate about inferential errors associated withp-values and statistical significance testing, readers are provided with a clear grasp of what statistical inference presupposes, and what it can and cannot do. To facilitate intuition, the representations throughout the book are as non-technical as possible.The central inspiration behind the text comes from the scientific debate about good statistical practices and the replication crisis. Calls for statistical reform include an unprecedented methodological warning from theAmerican Statistical Associationin 2016, a special issue 'Statistical Inference in the 21st Century:A World BeyondpThe American Statisticianin 2019, and a widely supported call to 'Retire statistical significance' inNaturein 2019.The book elucidates the probabilistic foundations and the potential of sample-based inferences, including random data generation, effect size estimation, and the assessment of estimation uncertainty caused by random error. Based on a thorough understanding of those basics, it then describes thep-value concept and the null-hypothesis-significance-testing ritual, and finally points out the ensuing inferential errors. This provides readers with the competence to avoid ill-guided statistical routines and misinterpretations of statistical quantities in the future.Intended for readers with an interest in understanding the role of statistical inference, the book provides a prudent assessment of the knowledge gain that can be obtained from a particular setof data under consideration of the uncertainty caused by random error. More particularly, it offers an accessible resource for graduate students as well as statistical practitioners who have a basic knowledge of statistics. Last but not least, it is aimed at scientists with a genuine methodological interest in the above-mentioned reform debate.…

Taschenbuch. Condición: Neu. Fundamentals of Statistical Inference | What is the Meaning of Random Error? | Norbert Hirschauer (u. a.) | Taschenbuch | SpringerBriefs in Applied Statistics and Econometrics | xv | Englisch | 2022 | Springer | EAN 9783030990909 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | - 1. Introduction. - 2. The Meaning of Scientific and Statistical Inference. - 3. The Basics of Statistical Inference: Simple Random Sampling. - 4. Estimation Uncertainty in Complex Sampling Designs. - 5. Knowledge Accumulation Through Meta-analysis and Replications. - 6. The p-Value and Statistical Significance Testing. - 7. Statistical Inference in Experiments. - 8. Better Inference in the 21st Century: A World Beyond p < 0.05. …

Paperback. Condición: New. 1st ed. 2022. This book provides a coherent description of foundational matters concerning statistical inference and shows how statistics can help us make inductive inferences about a broader context, based only on a limited dataset such as a random sample drawn from a larger population. By relating those basics to the methodological debate about inferential errors associated with p-values and statistical significance testing, readers are provided with a clear grasp of what statistical inference presupposes, and what it can and cannot do. To facilitate intuition, the representations throughout the book are as non-technical as possible.The central inspiration behind the text comes from the scientific debate about good statistical practices and the replication crisis. Calls for statistical reform include an unprecedented methodological warning from the American Statistical Association in 2016, a special issue "Statistical Inference in the 21st Century:A World Beyond p The American Statistician in 2019, and a widely supported call to "Retire statistical significance" in Nature in 2019.The book elucidates the probabilistic foundations and the potential of sample-based inferences, including random data generation, effect size estimation, and the assessment of estimation uncertainty caused by random error. Based on a thorough understanding of those basics, it then describes the p-value concept and the null-hypothesis-significance-testing ritual, and finally points out the ensuing inferential errors. This provides readers with the competence to avoid ill-guided statistical routines and misinterpretations of statistical quantities in the future.Intended for readers with an interest in understanding the role of statistical inference, the book provides a prudent assessment of the knowledge gain that can be obtained from a particular setof data under consideration of the uncertainty caused by random error. More particularly, it offers an accessible resource for graduate students as well as statistical practitioners who have a basic knowledge of statistics. Last but not least, it is aimed at scientists with a genuine methodological interest in the above-mentioned reform debate.…

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a coherent description of foundational matters concerning statistical inference and shows how statistics can help us make inductive inferences about a broader context, based only on a limited dataset such as a random sample drawn from a larger population. By relating those basics to the methodological debate about inferential errors associated withp-values and statistical significance testing, readers are provided with a clear grasp of what statistical inference presupposes, and what it can and cannot do. To facilitate intuition, the representations throughout the book are as non-technical as possible.The central inspiration behind the text comes from the scientific debate about good statistical practices and the replication crisis. Calls for statistical reform include an unprecedented methodological warning from theAmerican Statistical Associationin 2016, a special issue 'Statistical Inference in the 21st Century:A World BeyondpThe American Statisticianin 2019, and a widely supported call to 'Retire statistical significance' inNaturein 2019.The book elucidates the probabilistic foundations and the potential of sample-based inferences, including random data generation, effect size estimation, and the assessment of estimation uncertainty caused by random error. Based on a thorough understanding of those basics, it then describes thep-value concept and the null-hypothesis-significance-testing ritual, and finally points out the ensuing inferential errors. This provides readers with the competence to avoid ill-guided statistical routines and misinterpretations of statistical quantities in the future.Intended for readers with an interest in understanding the role of statistical inference, the book provides a prudent assessment of the knowledge gain that can be obtained from a particular setof data under consideration of the uncertainty caused by random error. More particularly, it offers an accessible resource for graduate students as well as statistical practitioners who have a basic knowledge of statistics. Last but not least, it is aimed at scientists with a genuine methodological interest in the above-mentioned reform debate. 148 pp. Englisch.…

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Facilitates effective intuition of what statistical inference presupposes, and what it can and cannot doElaborates on the ongoing debate about inferential errors related to p-values and statistical significance testingProvides a non-technic.…

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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a coherent description of foundational matters concerning statistical inference and shows how statistics can help us make inductive inferences about a broader context, based only on a limited dataset such as a random sample drawn from a larger population. By relating those basics to the methodological debate about inferential errors associated with p-values and statistical significance testing, readers are provided with a clear grasp of what statistical inference presupposes, and what it can and cannot do. To facilitate intuition, the representations throughout the book are as non-technical as possible.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 148 pp. Englisch.…