Significance Testing
DOWNLOAD
Download Significance Testing PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Significance Testing book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Tests Of Significance
DOWNLOAD
Author : Ramon E. Henkel
language : en
Publisher: SAGE
Release Date : 1976-09
Tests Of Significance written by Ramon E. Henkel and has been published by SAGE 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.
Understanding Significance Testing
DOWNLOAD
Author : Lawrence B. Mohr
language : en
Publisher: SAGE Publications, Incorporated
Release Date : 1990-02
Understanding Significance Testing written by Lawrence B. Mohr 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 1990-02 with Mathematics categories.
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.
Statistical Significance Testing For Natural Language Processing
DOWNLOAD
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
DOWNLOAD
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!
What If There Were No Significance Tests
DOWNLOAD
Author : Lisa L. Harlow
language : en
Publisher: Routledge
Release Date : 2016-03-02
What If There Were No Significance Tests written by Lisa L. Harlow and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-02 with Psychology categories.
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.
Hypothesis Testing
DOWNLOAD
Author : Arthur Taff
language : en
Publisher:
Release Date : 2019-07-16
Hypothesis Testing written by Arthur Taff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-16 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! Well, what are you waiting for? Grab your copy today by clicking the BUY NOW button at the top of this page!
Statistical Significance
DOWNLOAD
Author : John MacInnes
language : en
Publisher: SAGE
Release Date : 2019-01-21
Statistical Significance written by John MacInnes and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-21 with Social Science categories.
You can′t get anywhere in your statistics course without grasping statistical significance. it′s often seen as difficult but is actually a straightforward concept everyone can—and should—understand. Do your results mean something—or not? How can you measure it? Breaking it down into three building blocks, this Little Quick Fix shows students how to master: hypothesis testing normal distribution p values Students will learn how to understand the concept and also how to explain it for maximum effect in their essays and lab reports. Good for results—this is also a secret weapon for critical thinking. Little Quick Fix titles provide quick but authoritative answers to the problems, hurdles, and assessment points students face in the research course, project proposal, or design—whatever their methods learning is. Lively, ultra-modern design; full-colour, each page a tailored design. An hour′s read. Easy to dip in and out of with clear navigation enables the reader to find what she needs—quick. Direct written style gets to the point with clear language. Nothing needs to be read twice. No fluff. Learning is reinforced through a 2-minute overview summary; 3-second summaries with super-quick Q&A DIY tasks create a work plan to accomplish a task, do a self-check quiz, solve a problem, get students to what they need to show their supervisor. Checkpoints in each section make sure students are nailing it as they go and support self-directed learning. How do I know I’m done? Each Little Quick Fix wraps up with a final checklist that allows the reader to self-assess they’ve got what they need to progress, submit, or ace the test or task.
Hypothesis Testing Made Simple
DOWNLOAD
Author : Leonard Gaston Ph.D.
language : en
Publisher: Leonard Gaston Ph.D.
Release Date : 2014-03-11
Hypothesis Testing Made Simple written by Leonard Gaston Ph.D. and has been published by Leonard Gaston Ph.D. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-11 with Mathematics categories.
This tutorial, directed primarily toward students doing research projects, is intended to help them do four things : (1) Decide if their data gathering activity can yield numerical data that will permit a meaningful hypothesis test. (2) If it will, decide if one of the tests described would be useful. (3) If so, apply that test, and (4) Adequately explain the use of the test so their readers can have confidence in their analysis. It is not intended to be a text for a complete statistics course – only a guide to few relatively simple tests . Complex hypothesis testing procedures are not covered. For example, the discussion of Analysis of Variance (ANOVA) introduces what is called one way ANOVA. Two way ANOVA, a more complex procedure involving the use of blocking variables, is not covered. It simply presents a few commonly used tests and down-to-earth explanations of how to use them. It is intended to be a low cost supplement that can help its reader understand a few commonly-used tests. It was put together on a shoestring by a non-mathematician for the benefit of other non-mathematicians -- or for mathematicians who have forgotten some or all of the statistics they have studied. There are no color illustrations or professionally-prepared charts and graphs. Economy was a guiding principle. Four brief introductory chapters discuss numbers, basic terms, measures of central tendency and dispersion, probability, and data presentation. With that background the book then introduces various tests and explains them in down to earth language.
Essays In Hypothesis Testing With Instrumental Variables
DOWNLOAD
Author : Andrés Santos
language : en
Publisher:
Release Date : 2007
Essays In Hypothesis Testing With Instrumental Variables written by Andrés Santos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Statisitical Significance Testing
DOWNLOAD
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."