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Author: Lisa L. Harlow Publisher: Routledge ISBN: 131724284X Category : Psychology Languages : en Pages : 436
Book Description
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.
Author: Lisa L. Harlow Publisher: Routledge ISBN: 131724284X Category : Psychology Languages : en Pages : 436
Book Description
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.
Author: Rotem Dror Publisher: Springer Nature ISBN: 3031021746 Category : Computers Languages : en Pages : 98
Book Description
Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental. The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.
Author: John C. Reinard Publisher: SAGE Publications ISBN: 1506320481 Category : Language Arts & Disciplines Languages : en Pages : 604
Book Description
"While most books on statistics seem to be written as though targeting other statistics professors, John Reinard′s Communication Research Statistics is especially impressive because it is clearly intended for the student reader, filled with unusually clear explanations and with illustrations on the use of SPSS. I enjoyed reading this lucid, student-friendly book and expect students will benefit enormously from its content and presentation. Well done!" --John C. Pollock, The College of New Jersey Written in an accessible style using straightforward and direct language, Communication Research Statistics guides students through the statistics actually used in most empirical research undertaken in communication studies. This introductory textbook is the only work in communication that includes details on statistical analysis of data with a full set of data analysis instructions based on SPSS 12 and Excel XP. Key Features: Emphasizes basic and introductory statistical thinking: The basic needs of novice researchers and students are addressed, while underscoring the foundational elements of statistical analyses in research. Students learn how statistics are used to provide evidence for research arguments and how to evaluate such evidence for themselves. Prepares students to use statistics: Students are encouraged to use statistics as they encounter and evaluate quantitative research. The book details how statistics can be understood by developing actual skills to carry out rudimentary work. Examples are drawn from mass communication, speech communication, and communication disorders. Incorporates SPSS 12 and Excel: A distinguishing feature is the inclusion of coverage of data analysis by use of SPSS 12 and by Excel. Information on the use of major computer software is designed to let students use such tools immediately. Companion Web Site! A dedicated Web site includes a glossary, data sets, chapter summaries, additional readings, links to other useful sites, selected "calculators" for computation of related statistics, additional macros for selected statistics using Excel and SPSS, and extra chapters on multiple discriminant analysis and loglinear analysis. Intended Audience: Ideal for undergraduate and graduate courses in Communication Research Statistics or Methods; also relevant for many Research Methods courses across the social sciences
Author: Alexander Holmes Publisher: ISBN: Category : Business & Economics Languages : en Pages : 1801
Book Description
Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.
Author: Rex B. Kline Publisher: Amer Psychological Assn ISBN: 9781433812781 Category : Psychology Languages : en Pages : 349
Book Description
Traditional education in statistics that emphasises significance testing leaves researchers and students ill prepared to understand what their results really mean. Specifically, most researchers and students who do not have strong quantitative backgrounds have difficulty understanding outcomes of statistical tests. As more and more people become aware of this problem, the emphasis on statistical significance in the reporting of results is declining. Increasingly, researchers are expected to describe the magnitudes and precisions of their findings and also their practical, theoretical, or clinical significance. This accessibly written book reviews the controversy about significance testing, which has now crossed various disciplines as diverse as psychology, ecology, commerce, education, and biology, among others. It also introduces readers to alternative methods, especially effect size estimation (at both the group and case levels) and interval estimation (confidence intervals) in comparative studies. Basics of bootstrapping and Bayesian estimation are also considered. Research examples from substance abuse, education, learning, and other areas illustrate how to apply these methods. A companion website promotes learning by providing chapter exercises and sample answers, downloadable raw data files for many research examples, and links to other useful websites. New to this edition is coverage of robust statistical methods for parameter estimation, effect size estimation, and interval estimation. A new chapter covers the logic and illogic of significance testing. This edition also addresses recent developments such as the new requirements of some journals for the reporting of effect sizes.
Author: David S. Moore Publisher: Palgrave Macmillan ISBN: 1429224266 Category : Mathematics Languages : en Pages : 975
Book Description
This is a clear and innovative overview of statistics which emphasises major ideas, essential skills and real-life data. The organisation and design has been improved for the fifth edition, coverage of engaging, real-world topics has been increased and content has been updated to appeal to today's trends and research.
Author: Douglas Altman Publisher: John Wiley & Sons ISBN: 1118702506 Category : Medical Languages : en Pages : 322
Book Description
This highly popular introduction to confidence intervals has been thoroughly updated and expanded. It includes methods for using confidence intervals, with illustrative worked examples and extensive guidelines and checklists to help the novice.
Author: Scott Hartshorn Publisher: ISBN: 9781973181460 Category : Languages : en Pages : 106
Book Description
Hypothesis Testing & Statistical Significance If you are looking for a short beginners guide packed with visual examples, this booklet is for you. Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their way into scientific and engineering tests of all kinds, from medical tests with control group and a testing group, to the analysis of how strong a newly made batch of parts is. Those same calculations are also used in investment decisions. This book goes through all the major types of statistical significance calculations, and works through an example using them, and explains when you would use that specific type instead of one of the others. Just as importantly, this book is loaded with visual examples of what exactly statistical significance is, and the book doesn't assume that you have prior in depth knowledge of statistics or that use regularly use an advanced statistics software package. If you know what an average is and can use Excel, this book will build the rest of the knowledge, and do so in an intuitive way. For instance did you know that Statistical Significance Can Be Easily Understood By Rolling A Few Dice? In fact, you probably already know this key concept in statistical significance, although you might not have made the connection. The concept is this. Roll a single die. Is any number more likely to come up than another ? No, they are all equally likely. Now roll 2 dice and take their sum. Suddenly the number 7 is the most likely sum (which is why casinos win on it in craps). The probability of the outcome of any single die didn't change, but the probability of the outcome of the average of all the dice rolled became more predictable. If you keep increasing the number of dice rolled, the outcome of the average gets more and more predictable. This is the exact same effect that is at the heart of all the statistical significance equations (and is explained in more detail in the book) You Are Looking At Revision 2 Of This Book The book that you are looking at on Amazon right now is the second revision of the book. Earlier I said that you might have missed the intuitive connections to statistical significance that you already knew. Well that is because I missed them in the first release of this book. The first release included examples for the major types of statistical significance A Z-Test A 1 Sample T-Test A Paired T Test A 2 Sample T-Test with equal variance A 2 Sample T-test with unequal variance Descriptions of how to use a T-table and a Z-table And those examples were good for what they were, but were frankly not significantly different than you could find in many statistics textbooks or on Wikipedia. However this revision builds on those examples, draws connections between them, and most importantly explains concepts such as the normal curve or statistical significance in a way that will stick with you even if you don't remember the exact equation. If you are a visual learner and like to learn by example, this intuitive booklet might be a good fit for you. Statistical Significance is fascinating topic and likely touches your life every single day. It is a very important tool that is used in data analysis throughout a wide-range of industries - so take an easy dive into the topic with this visual approach!
Author: Lisa L. Harlow
Author: Lisa L. Harlow
Author: Rotem Dror
Author: John C. Reinard
Author: Alexander Holmes
Author: Ramon E. Henkel
Author: Stephen Thomas Ziliak
Author: Rex B. Kline
Author: David S. Moore
Author: Douglas Altman
Author: Scott Hartshorn