Statisitical Significance Testing
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Tests Of Significance
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Author : Ramon E. Henkel
language : en
Publisher: SAGE Publications, Incorporated
Release Date : 1976-09
Tests Of Significance written by Ramon E. Henkel and has been published by SAGE Publications, Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 1976-09 with Business & Economics categories.
An elementary introduction to significance testing, this paper provides a conceptual and logical basis for understanding these tests.
Statistical Significance
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Author : John MaccInes
language : en
Publisher:
Release Date :
Statistical Significance written by John MaccInes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Social scientists often want to know if a finding is statistically significant, discuss the p-values or put confidence intervals around results. This course explains what these terms mean, how they are calculated, and how their origin lies in the way we use samples to measure and investigate people, organizations and societies. By the end of this course, learners will be able to: Understand the definition of and factors involved in establishing statistical significance Recognize the importance of inference and how we gain information about populations from samples Define, interpret, and calculate normal distribution Establish the validity of sample estimates through calculating and interpreting the standard error Use confidence intervals to identify a range of samples that will include the population parameter under investigation Define and calculate the p-value in order to interpret the statistical significance of your null hypothesis Recognize and evaluate what the p-value can tell us about our research.
Statistical Hypothesis Testing
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Author : Ning-Zhong Shi
language : en
Publisher: World Scientific
Release Date : 2008
Statistical Hypothesis Testing written by Ning-Zhong Shi and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Science categories.
This book presents up-to-date theory and methods of statistical hypothesis testing based on measure theory. The so-called statistical space is a measurable space adding a family of probability measures. Most topics in the book will be developed based on this term. The book includes some typical data sets, such as the relation between race and the death penalty verdict, the behavior of food intake of two kinds of Zucker rats, and the per capita income and expenditure in China during the 1978?2002 period. Emphasis is given to the process of finding appropriate statistical techniques and methods of evaluating these techniques.
Statistical Significance Testing For Natural Language Processing
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Author : Rotem Dror
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Statistical Significance Testing For Natural Language Processing written by Rotem Dror and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
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 drivesthe 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.
Hypothesis Testing
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Author : Scott Hartshorn
language : en
Publisher:
Release Date : 2017-10-29
Hypothesis Testing written by Scott Hartshorn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-29 with categories.
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!
Hypothesis Testing
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Author : Arthur Taff
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-03-08
Hypothesis Testing written by Arthur Taff and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-08 with categories.
The Perfect Book for Beginners Wanting to Learn About Hypothesis Testing & Statistical Significance! Multi-time best selling IT & mathematics author, Arthur Taff, presents a leading book for beginners to learn and understand hypothesis testing - specifically statistical significance. Statistical significance is a way of determining if an outcome occurred by random chance, or if something caused 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. In this book, you will get: A breakdown of all the major types of statistical significance calculations, and workings through an example using them, with an explanation of when you would use that specific type instead of one of the others. Visual examples included with all explanations, so you can better understand and learn statistical significance. An easy-to-understand approach that doesn't assume you have prior in-depth knowledge of statistics or that you regularly use an advanced statistics software package. The quickest hack to hypothesis testing - if you know what an "average" is and can use Excel at a basic level, this book will build the rest of the knowledge, and do so in an intuitive way. Arthur's personal email address for unlimited customer support if you have any questions And much, much more... If you are a person that learns by example, then this book is perfect for you! It is a very important topic with use in a wide range of industries and situations - so dive in to get a deep understanding!
The Significance Test Controversy
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Author : Ramon E. Henkel
language : en
Publisher: Routledge
Release Date : 2017-07-28
The Significance Test Controversy written by Ramon E. Henkel and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Psychology categories.
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.
Statistical Power Analysis
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Author : Kevin R. Murphy
language : en
Publisher: Routledge
Release Date : 2003-08-01
Statistical Power Analysis written by Kevin R. Murphy and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-01 with Psychology categories.
This book presents a simple and general method for conducting statistical power analysis based on the widely used F statistic. The book illustrates how these analyses work and how they can be applied to problems of studying design, to evaluate others' research, and to choose the appropriate criterion for defining "statistically significant" outcomes. Statistical Power Analysis examines the four major applications of power analysis, concentrating on how to determine: *the sample size needed to achieve desired levels of power; *the level of power that is needed in a study; *the size of effect that can be reliably detected by a study; and *sensible criteria for statistical significance. Highlights of the second edition include: a CD with an easy-to-use statistical power analysis program; a new chapter on power analysis in multi-factor ANOVA, including repeated-measures designs; and a new One-Stop PV Table to serve as a quick reference guide. The book discusses the application of power analysis to both traditional null hypothesis tests and to minimum-effect testing. It demonstrates how the same basic model applies to both types of testing and explains how some relatively simple procedures allow researchers to ask a series of important questions about their research. Drawing from the behavioral and social sciences, the authors present the material in a nontechnical way so that readers with little expertise in statistical analysis can quickly obtain the values needed to carry out the power analysis. Ideal for students and researchers of statistical and research methodology in the social, behavioral, and health sciences who want to know how to apply methods of power analysis to their research.
Statisitical Significance Testing
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Author : Ben Carterette
language : en
Publisher: Morgan & Claypool
Release Date : 2014-09-30
Statisitical Significance Testing written by Ben Carterette and has been published by Morgan & Claypool this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-30 with Business & Economics categories.
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."
Studying A Study And Testing A Test
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Author :
language : en
Publisher: Lippincott Williams & Wilkins
Release Date : 2005
Studying A Study And Testing A Test written by and has been published by Lippincott Williams & Wilkins this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Medical categories.
Now in its Fifth Edition, this best-selling text presents a step-by-step approach to critical and efficient reading of the medical literature. Health care professionals will learn how to evaluate clinical studies, identify flaws in study design, interpret statistics, and apply evidence from clinical research in practice. This edition's new section, Guide to the Guidelines, reflects the growing use and importance of clinical guidelines. The outcomes research chapter includes concepts of safety and effects of interactions on outcomes. This edition also presents statistics more graphically. Unique learning aids include question checklists, scenarios illustrating study design, and flaw-catching exercises, plus a StudyingaStudy.com Website providing interactive materials.