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Library of Congress Cataloging-in-Publication Data
Names: Jawlik, Andrew.
Title: Statistics from A to Z : confusing concepts clarified / Andrew Jawlik.
Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2016].
Identifiers: LCCN 2016017318 | ISBN 9781119272038 (pbk.) | ISBN 9781119272007 (epub)
Subjects: LCSH: Mathematical statistics–Dictionaries. | Statistics–Dictionaries.
Classification: LCC QA276.14 .J39 2016 | DDC 519.503–dc23
LC record available at https://lccn.loc.gov/2016017318
To my wonderful wife, Jane, who is a 7 Sigma*.
or s.
Statistics can be confusing – even for smart people, and even for smart technical people.
As an illustration, how quickly can we figure out whether the woman pictured above agreed to get married? (For the answer, see the article in this book, “Fail to Reject the Null Hypothesis.”)
This is understandable, not only because some of the concepts are inherently complicated and difficult to understand, but also because:
For example, the Dependent Variable, the Outcome, the Effect, the Response, and the Criterion are all the same thing. And – believe it or not – there are at least seven different names and 18 different acronyms used for just the three Statistics: Sum of Squares Between, Sum of Squares Within, and Sum of Squares Total.
Synonyms may be wonderful for poets and fiction writers, but they confuse things unnecessarily for students and practitioners of a technical discipline.
For example, “SST” is variously used for “Sum of Squares Total” or “Sum of Squares Treatment.” (The latter is actually a component part of the former.)
The acknowledged experts sometimes disagree on fundamental concepts. For example, some experts specify the use of the Alternative Hypothesis in their methods of Hypothesis Testing. Others are “violently opposed” to its use. Other experts recommend avoiding Hypothesis Testing completely, because of the confusing language.
The meaning of words in statistics can sometimes be very different from, or even the opposite of, the meaning of the same words in normal, everyday language.
For example, in a Bernoulli experiment on process quality, a quality failure is called a “success.” Also, for Skew or Skewness, in statistics, “left” means right.
Which Distribution do I use when? Which Test Statistic? Which test? Which Control Chart? Which type of graph?
There are several choices for each – some of which are good in a given situation, some not.
Even those with titles targeting the supposedly clueless reader do not provide sufficient explanation to clear up a lot of this confusion. Students and professionals continue to look for a book which would give them a true intuitive understanding of statistical concepts.
Also, if you look up a concept in the index of other books, you will find something like this:
“Degrees of freedom, 60, 75, 86, 91–93, 210, 241”
So, you have to go to six different places, pick up the bits and pieces from each, and try to assemble for yourself some type of coherent concept. In this book, each concept is completely covered in one or more contiguous short articles (usually three to seven pages each). And we don't need an index, because you find the concepts alphabetically – as in a dictionary or encyclopedia.
It is much easier to understand than other books on the subject, because of the following:
Individual articles which completely treat one concept per article (or series of contiguous articles). No paging through the book for bits and pieces here and there.
Almost all the articles start with a one-page summary of five or so Keys to Understanding, which gives you the whole picture on a single page. The remaining pages in the article provide a more in-depth explanation of each of the individual keys.
Wider Scope – Statistics I and Statistics II and Six Sigma Black Belt. Most books are focused on statistics in the social sciences, and – to a lesser extent – physical sciences or management. They don't cover statistical concepts important in process and quality improvement (Six Sigma or industrial engineering).
Authored by a recent student, who is freshly aware of the statistical concepts that confused him – and why. (The author recently completed a course of study for professional certification as a Lean Six Sigma black belt – a process and quality improvement discipline which uses statistics extensively. He had, years earlier, earned an MS in Mathematics in a concentration which did not include much statistics content.)
Use this book when:
It can be useful:
To find a subject, you can flip through the book like an old dictionary or encyclopedia volume. If the subject you are looking for does not have an article devoted to it, there is likely a glossary description for it. And/or it may be covered in an article on another subject. In an alphabetically-organized book like this, the Contents and the Other Concepts pages make an Index unnecessary.
See the Contents at the beginning of this book for a list of the articles covering the major concepts. Following the Contents is a section called “Other Concepts Covered in the Articles.” Here, you can find concepts which do not headline their own articles, for example:
Acceptance Region: see the article Alpha, α.
If you have a statistical problem to solve or question to answer and don't know how to go about it, see the article Which Statistical Tool to Use to Solve Some Common Problems. There are also Which to Use When articles for Distributions, Control Charts, and Charts/Graphs/Plots.
This book is designed for use as a reference for looking up specific topics, not as a textbook to be read front-to-back. However, if you do want to use this book as a single source for learning statistics, not just a reference, you could read the following articles in the order shown:
At the end of these and all other articles in the book is a list of Related Articles which you can read for more detail on related subjects.