Tests of Significance

An elementary introduction to significance testing, this paper provides a conceptual and logical basis for understanding these tests.

Author: Ramon E. Henkel

Publisher: SAGE

ISBN: 9780803906525

Category: Business & Economics

Page: 92

View: 297

An elementary introduction to significance testing, this paper provides a conceptual and logical basis for understanding these tests.

What If There Were No Significance Tests

The classic edition of What If There Were No Significance Tests? highlights current statistical inference practices.

Author: Lisa L. Harlow

Publisher: Routledge

ISBN: 1317242858

Category: Psychology

Page: 396

View: 457

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.

The Significance Test Controversy Revisited

The purpose of this book is not only to revisit the “significance test controversy,”but also to provide a conceptually sounder alternative.

Author: Bruno Lecoutre

Publisher: Springer

ISBN: 3662440466

Category: Mathematics

Page: 134

View: 147

The purpose of this book is not only to revisit the “significance test controversy,”but also to provide a conceptually sounder alternative. As such, it presents a Bayesian framework for a new approach to analyzing and interpreting experimental data. It also prepares students and researchers for reporting on experimental results. Normative aspects: The main views of statistical tests are revisited and the philosophies of Fisher, Neyman-Pearson and Jeffrey are discussed in detail. Descriptive aspects: The misuses of Null Hypothesis Significance Tests are reconsidered in light of Jeffreys’ Bayesian conceptions concerning the role of statistical inference in experimental investigations. Prescriptive aspects: The current effect size and confidence interval reporting practices are presented and seriously questioned. Methodological aspects are carefully discussed and fiducial Bayesian methods are proposed as a more suitable alternative for reporting on experimental results. In closing, basic routine procedures regarding the means and their generalization to the most common ANOVA applications are presented and illustrated. All the calculations discussed can be easily carried out using the freeware LePAC package.

Selected Statistical Tests

This Book Will Help Them To Interpret Their Data Themselves In A Better Manner. In This Book, Frequently Used Statistical Tests Are Presented In A Simple And Understandable Way With Real Life Examples And Exercises.

Author: V. Rajagopalan

Publisher: New Age International

ISBN: 8122418406

Category: Statistical hypothesis testing

Page: 260

View: 252

This Book Provides Many Kinds Of Statistical Tests Available In Statistics, Which Are Widely Used In Various Disciplines, Especially Very Much Useful For The Researchers Who Need Statistical Tools And Techniques For Their Data Analysis. This Book Will Help Them To Interpret Their Data Themselves In A Better Manner. In This Book, Frequently Used Statistical Tests Are Presented In A Simple And Understandable Way With Real Life Examples And Exercises.

Hypothesis Testing

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.

Author: Scott Hartshorn

Publisher:

ISBN: 9781973181460

Category:

Page: 106

View: 700

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!

Applied Adaptive Statistical Methods

Applied Adaptive Statistical Methods: Tests of Significance and Confidence Intervals introduces many of the practical adaptive statistical methods developed over the last 10 years and provides a comprehensive approach to tests of ...

Author: Thomas W. O'Gorman

Publisher: SIAM

ISBN: 9780898718430

Category: Adaptive sampling (Statistics)

Page: 174

View: 945

Adaptive statistical tests, developed over the last 30 years, are often more powerful than traditional tests of significance, but have not been widely used. To date, discussions of adaptive statistical methods have been scattered across the literature and generally do not include the computer programs necessary to make these adaptive methods a practical alternative to traditional statistical methods. Until recently, there has also not been a general approach to tests of significance and confidence intervals that could easily be applied in practice. Modern adaptive methods are more general than earlier methods and sufficient software has been developed to make adaptive tests easy to use for many real-world problems. Applied Adaptive Statistical Methods: Tests of Significance and Confidence Intervals introduces many of the practical adaptive statistical methods developed over the last 10 years and provides a comprehensive approach to tests of significance and confidence intervals. It shows how to make confidence intervals shorter and how to make tests of significance more powerful by using the data itself to select the most appropriate procedure.


The Significance Test Controversy

This book gathers the central papers in this continuing debate, brings the issues into clear focus, points out practical problems and philosophical pitfalls involved in using the tests, and provides a benchmark from which further analysis ...

Author: Ramon E. Henkel

Publisher: Routledge

ISBN: 1351474162

Category: Psychology

Page: 347

View: 663

Tests of significance have been a key tool in the research kit of behavioral scientists for nearly fifty years, but their widespread and uncritical use has recently led to a rising volume of controversy about their usefulness. This book gathers the central papers in this continuing debate, brings the issues into clear focus, points out practical problems and philosophical pitfalls involved in using the tests, and provides a benchmark from which further analysis can proceed.The papers deal with some of the basic philosophy of science, mathematical and statistical assumptions connected with significance tests and the problems of the interpretation of test results, but the work is essentially non-technical in its emphasis. The collection succeeds in raising a variety of questions about the value of the tests; taken together, the questions present a strong case for vital reform in test use, if not for their total abandonment in research.The book is designed for practicing researchers-those not extensively trained in mathematics and statistics that must nevertheless regularly decide if and how tests of significance are to be used-and for those training for research. While controversy has been centered in sociology and psychology, and the book will be especially useful to researchers and students in those fields, its importance is great across the spectrum of the scientific disciplines in which statistical procedures are essential-notably political science, economics, and the other social sciences, education, and many biological fields as well.Denton E. Morrison is professor, Department of Sociology, Michigan State University.Ramon E. Henkel is associate professor emeritus, Department of Sociology University of Maryland. He teaches as part of the graduate faculty.

Adaptive Tests of Significance Using Permutations of Residuals with R and SAS

The book also serves as a valuable supplement for courses on regression analysis and adaptive analysis at the upper-undergraduate and graduate levels.

Author: Thomas W. O'Gorman

Publisher: John Wiley & Sons

ISBN: 0470922257

Category: Mathematics

Page: 345

View: 189

Provides the tools needed to successfully perform adaptive tests across a broad range of datasets Adaptive Tests of Significance Using Permutations of Residuals with R and SAS illustrates the power of adaptive tests and showcases their ability to adjust the testing method to suit a particular set of data. The book utilizes state-of-the-art software to demonstrate the practicality and benefits for data analysis in various fields of study. Beginning with an introduction, the book moves on to explore the underlying concepts of adaptive tests, including: Smoothing methods and normalizing transformations Permutation tests with linear methods Applications of adaptive tests Multicenter and cross-over trials Analysis of repeated measures data Adaptive confidence intervals and estimates Throughout the book, numerous figures illustrate the key differences among traditional tests, nonparametric tests, and adaptive tests. R and SAS software packages are used to perform the discussed techniques, and the accompanying datasets are available on the book's related website. In addition, exercises at the end of most chapters enable readers to analyze the presented datasets by putting new concepts into practice. Adaptive Tests of Significance Using Permutations of Residuals with R and SAS is an insightful reference for professionals and researchers working with statistical methods across a variety of fields including the biosciences, pharmacology, and business. The book also serves as a valuable supplement for courses on regression analysis and adaptive analysis at the upper-undergraduate and graduate levels.

The Use of Restricted Significance Tests in Clinical Trials

The reader will soon find that this is more than a "how-to-do-it" book.

Author: David S. Salsburg

Publisher: Springer Science & Business Media

ISBN: 1461244145

Category: Mathematics

Page: 174

View: 996

The reader will soon find that this is more than a "how-to-do-it" book. It describes a philosophical approach to the use of statistics in the analysis of clinical trials. I have come gradually to the position described here, but I have not come that way alone. This approach is heavily influenced by my reading the papers of R.A. Fisher, F.S. Anscombe, F. Mosteller, and J. Neyman. But the most important influences have been those of my medical colleagues, who had important real-life medical questions that needed to be answered. Statistical methods depend on abstract mathematical theorems and often complicated algorithms on the computer. But these are only a means to an end, because in the end the statistical techniques we apply to clinical studies have to provide useful answers. When I was studying martingales and symbolic logic in graduate school, my wife, Fran, had to be left out of the intellectual excitement. But, as she looked on, she kept asking me how is this knowledge useful. That question, what can you do with this? haunted my studies. When I began working in bio statistics, she continued asking me where it was all going, and I had to explain what I was doing in terms of the practical problems that were being ad dressed.

What If There Were No Significance Tests

The classic edition of What If There Were No Significance Tests? highlights current statistical inference practices.

Author: Lisa L. Harlow

Publisher: Routledge

ISBN: 131724284X

Category: Psychology

Page: 396

View: 326

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.

The Significance Test Controversy

This book gathers the central papers in this continuing debate, brings the issues into clear focus, points out practical problems and philosophical pitfalls involved in using the tests, and provides a benchmark from which further analysis ...

Author: Ramon E. Henkel

Publisher: Routledge

ISBN: 9781138538535

Category:

Page: 347

View: 470

Tests of significance have been a key tool in the research kit of behavioral scientists for nearly fifty years, but their widespread and uncritical use has recently led to a rising volume of controversy about their usefulness. This book gathers the central papers in this continuing debate, brings the issues into clear focus, points out practical problems and philosophical pitfalls involved in using the tests, and provides a benchmark from which further analysis can proceed.The papers deal with some of the basic philosophy of science, mathematical and statistical assumptions connected with significance tests and the problems of the interpretation of test results, but the work is essentially non-technical in its emphasis. The collection succeeds in raising a variety of questions about the value of the tests; taken together, the questions present a strong case for vital reform in test use, if not for their total abandonment in research.The book is designed for practicing researchers-those not extensively trained in mathematics and statistics that must nevertheless regularly decide if and how tests of significance are to be used-and for those training for research. While controversy has been centered in sociology and psychology, and the book will be especially useful to researchers and students in those fields, its importance is great across the spectrum of the scientific disciplines in which statistical procedures are essential-notably political science, economics, and the other social sciences, education, and many biological fields as well.Denton E. Morrison is professor, Department of Sociology, Michigan State University.Ramon E. Henkel is associate professor emeritus, Department of Sociology University of Maryland. He teaches as part of the graduate faculty.

Statisitical Significance Testing

Our goal with this work is to help researchers and developers gain a better understanding of how tests work and how they should be interpreted so that they can both use them more effectively in their day-to-day work as well as better ...

Author: Ben Carterette

Publisher: Morgan & Claypool

ISBN: 9781627055277

Category: Business & Economics

Page:

View: 955

The past 20 years have seen a great improvement in the rigor of information retrieval experimentation, due primarily to two factors: high-quality, public, portable test collections such as those produced by TREC (the Text REtrieval Conference), and the increased practice of statistical hypothesis testing to determine whether measured improvements can be ascribed to something other than random chance. Together these create a very useful standard for reviewers, program commit- tees, and journal editors; work in information retrieval (IR) increasingly cannot be published unless it has been evaluated using a well-constructed test collection and shown to produce a statistically significant improvement over a good baseline. But, as the saying goes, any tool sharp enough to be useful is also sharp enough to be dangerous. Statistical tests of significance are widely misunderstood. Most researchers and developers treat them as a black box": evaluation results go in and a p-value comes out. But because significance is such an important factor in determining what research directions to explore and what is published, using p-values obtained without thought can have consequences for everyone doing research in IR. Ioannidis has argued that the main consequence in the biomedical sciences is that most published research findings are false; could that be the case in IR as well? Our goal with this work is to help researchers and developers gain a better understanding of how tests work and how they should be interpreted so that they can both use them more effectively in their day-to-day work as well as better understand how to interpret them when reading the work of others. We will do this primarily with three tools: (a) mathematical analysis; (b) simulation; and (c) experimentation with TREC data - because of the availability of TREC data, IR as a field is uniquely positioned to be able to evaluate significance testing in the presence of a wide variety of failed" experiments."



Hypothesis Testing and Model Selection in the Social Sciences

Examining the major approaches to hypothesis testing and model selection, this book blends statistical theory with recommendations for practice, illustrated with real-world social science examples.

Author: David L. Weakliem

Publisher: Guilford Publications

ISBN: 1462525652

Category: Social Science

Page: 202

View: 246

Examining the major approaches to hypothesis testing and model selection, this book blends statistical theory with recommendations for practice, illustrated with real-world social science examples. It systematically compares classical (frequentist) and Bayesian approaches, showing how they are applied, exploring ways to reconcile the differences between them, and evaluating key controversies and criticisms. The book also addresses the role of hypothesis testing in the evaluation of theories, the relationship between hypothesis tests and confidence intervals, and the role of prior knowledge in Bayesian estimation and Bayesian hypothesis testing. Two easily calculated alternatives to standard hypothesis tests are discussed in depth: the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The companion website ([ital]www.guilford.com/weakliem-materials[/ital]) supplies data and syntax files for the book's examples.

Understanding Significance Testing

Significance testing - a core technique in statistics for hypothesis testing - is introduced in this volume.

Author: Lawrence B. Mohr

Publisher: SAGE

ISBN: 9780803935686

Category: Mathematics

Page: 76

View: 769

Significance testing - a core technique in statistics for hypothesis testing - is introduced in this volume. Mohr first reviews what is meant by sampling and probability distributions and then examines in-depth normal and t-tests of significance. The uses and misuses of significance testing are also explored.



Hypothesis Testing Statistical Significance

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.

Author: Daniell Groberg

Publisher: Independently Published

ISBN:

Category:

Page: 140

View: 879

Statistical significance refers to the claim that a result from data generated by testing or experimentation is not likely to occur randomly or by chance but is instead likely to be attributable to a specific cause. Statistical significance calculations find their way into scientific and engineering tests of all kinds, from medical tests with the 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.