Hypothesis Testing Statistical Significance
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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!
Statistical Significance
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Author : Siu L Chow
language : en
Publisher: SAGE
Release Date : 1996
Statistical Significance written by Siu L Chow and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Social Science categories.
This comprehensive and accessible book provides an overview of the central and most fundamental methodological issue for empirical researchers - how should we interpret statistical significance? Beginning with a thorough introduction to null-hypothesis testing and statistical significance, the book then advances the arguments for and against the current interpretations and the use of significance testing in research. Siu L Chow presents a coherent challenge to contemporary criticisms of significance testing and offers a substantial and thought-provoking contribution to the debate on the proper role of statistical significance in empirical research.
Tests Of Significance
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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.
Hypothesis Testing
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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
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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.
What If There Were No Significance Tests
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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.
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.
Statistical Power Analysis
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Author : Kevin R. Murphy
language : en
Publisher: Routledge
Release Date : 2011-04-27
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 2011-04-27 with Education categories.
First Published in 2009. Routledge is an imprint of Taylor & Francis, an informa company.
Statistical Power Analysis
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Author : Brett Myors
language : en
Publisher: Routledge
Release Date : 2014-05-16
Statistical Power Analysis written by Brett Myors and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-16 with Psychology categories.
Noted for its accessible approach, this text applies the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. Through the use of a few simple procedures and examples, the authors show readers with little expertise in statistical analysis how to obtain the values needed to carry out the power analysis for their research. Illustrations of how these analyses work and how they can be used to choose the appropriate criterion for defining statistically significant outcomes are sprinkled throughout. The book presents a simple and general model for statistical power analysis based on the F statistic and reviews how to determine: the sample size needed to achieve desired levels of power; the level of power needed in a study; the size of effect that can be reliably detected by a study; and sensible criteria for statistical significance. The book helps readers design studies, diagnose existing studies, and understand why hypothesis tests come out out the way they do. The fourth edition features: -New Boxed Material sections provide examples of power analysis in action and discuss unique issues that arise as a result of applying power analyses in different designs. -Many more worked examples help readers apply the concepts presented. -Expanded coverage of power analysis for multifactor analysis of variance (ANOVA) to show readers how to analyze up to four factors with repeated measures on any or all of the factors. -Re-designed and expanded web based One Stop F Calculator software and data sets that allow users to perform all of the book's analyses and conduct significance tests, power analyses, and assessments of N and alpha needed for traditional and minimum-effects tests. -Easy to apply formulas for approximating the number of subjects required to reach adequate levels of power in a wide range of studies. Intended as a supplement for graduate/advanced undergraduate courses in research methods or experimental design, intermediate, advanced, or multivariate statistics, statistics II, or psychometrics, taught in psychology, education, business, and other social and health sciences, researchers also appreciate the book‘s applied approach.
Hypothesis Testing Statistical Significance
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Author : Daniell Groberg
language : en
Publisher: Independently Published
Release Date : 2021-07-23
Hypothesis Testing Statistical Significance written by Daniell Groberg and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-23 with categories.
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.