Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

This is an excellent book discussing statistical hypothesis testing. It takes a practical perspective, illustrated with plots and graphs, to convey ideas. His discussion at the end of the book regarding nonparametric methods and bootstrapping a sample were short but useful indicating when and why these methods can be used.I especially liked the chapters on interpreting p-values and types of error and statistical power. There are numerous examples of research that confuse p-values with the probability of a type 1 error when in fact it is the probability of getting the results for the given sample if the null hypothesis is true (i.e. no change). Actually the probability of a type 1 error is the significance level alpha set before any results are gathered and is based on the researcher’s prior domain knowledge. So it’s an educated guess. Further the author demonstrates the effect of sample size on results by showing how too small a sample can yield exaggerated results (since it takes more extreme values to make the results statistically significant) and very large samples can make small differences statistically significant. This discussion underlies the fact that study replication (with appropriate sample size) is essential before coming to any reliable conclusions. It also shows the time, expense, and effort genuinely required to perform viable scientific research. Sadly today’s scientific research tends to play fast and loose with statistical significance undermining confidence in published studies which often turn out to be “just so” stories wrapped in technical jargon.One other observation about the book is the author repeatedly called upon the analyst to exercise significant domain knowledge to evaluate the statistical results drawn from various methods (e.g. statistical power, p-value quality, significance level, outlier identification, continuous variable probability distribution identification, etc) in order to produce correct results. It was striking to me to see this insistence on human expertise to provide overall guidance of result evaluation and interpretation in an age where company management often insists on replacing humans with automation at every conceivable turn to reduce costs and increase profits. Taking this author seriously would question the quality of the results of this management approach.

I obtained my PhD in industrial engineering last year and joined industry upon graduation. I teach statistics at one of the commonwealth campuses of a large university and not only do I think this book does a phenomenal job of covering statistics as a practitioner (which believe me, many of my colleagues have forgotten and we have SO many uses for stats in our field), but I think every statistics instructor should read this book. Most statistics curriculums focus on calculations and cramming a bunch of concepts down a student’s throat. While that’s fine and all, I’ve always strived to drive home the point of “why and HOW could I use this in a practical sense, and what am I being taught that’s truly practical in most situations?” This book covers just that…it’s no lie that most students will move on from statistics and forget most things, but the conceptual parts of this book are what make statistics more memorable and could be great points to bring up in a statistics course to make students better and more informed consumers of statistics (and will hopefully better prepare those who are going to go to grad school, as I suffered the fate of not having a good stats background in my undergrad years). If you’re a grad student and you’re working on research in any capacity, you should read this book, too. I can’t recommend it enough and I look forward to reading more of Mr. Frost’s works.

Jim Frost has done a great job making a very difficult subject accessible to all. This book and his other two books on statistics ( Introduction to Statistics and Regression Analysis) are well-worth reading from cover to cover and then having on hand as invaluable reference materials. His books are easy to read and arcane topics easy to comprehend. These three books are especially relevant to undergraduates in the social sciences (economics, data analytics) and other fields where statistical methods are used heavily. The author also has a website that provides periodic postings on topics relating to specific statistic tests, sampling methods, and other aspects of statistics - for free!

I wish I had discovered this book while I was still in grad school. The writing is accessible and the concepts clearly articulated. It 100% improved my understanding of hypothesis testing and was pretty easy to read. I strongly recommend it to anyone seeking to advance their understanding of statistics.My only criticisms are that some of the figures would have been easier to interpret in color than grayscale, but I understand color printing would have driven up the cost of the book by a lot, so this is understandable. I also think the author should have addressed the five or more rule when discussing chi square testing.

This book summarizes commonly used hypothesis testings for researchers in statistics, economics, business, biology, etc, who need to design and answer hypothesis testing questions in their work, and it gives introduction of these hypothesis tests in an easy to understand but solid way- it does touch the theoretical foundations in beginning chapters and mention the theories for each test through out the chapters to keep the depth of the book. The implementation is also described in flow. It is light and thin, but very informative and useful.It helps me a lot in my study design in a handy way! Recommend for entry to intermediate level statisticians, economists, researchers, etc who need to answer hypothesis testing questions in their studies.

By reading this book you will gain a deep knowledge of hypothesis testing and above all you will avoid common mistakes in its application.

I've taught statistics at university and in the workplace for over 30 years. This is by far the best introductory book I've come across. Does a fantastic job of explaining basic concepts in simple language.