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You can view the test as a very entertaining IQ test from hell. The questions seem often simple. But, they are not. Other times, they are obviously difficult. I got a bit more than half of them correct. This was mainly because I had some knowledge or experience regarding certain traps the questions presented. I had made the mistake before. So, I learned from that. When I did not have any prior knowledge of a question, my results were very human, meaning not that good. But, learning the correct answer was both fun and educating.
The author touches on several fascinating probability and statistic concepts. One of them being the Bayes theorem, which suggests that medical screen test can be highly unreliable despite being touted as 80% to 90% accurate. In other words, you better understand the Bayes theorem better than the medical specialists who screen you for various diseases. Because, based on the author's study, doctors don't have a clue. Another chapter had an excellent discussion on correlation vs. causation. This includes some tricky nuances that many analysts in the financial industry trip upon. Another interesting probability concept is why it takes only 23 people in a room to have greater than a 50% that two of them share the same birthday. This seems impossible, but it is true.
The book has obviously a lot more than I am letting on here. I am not going to ruin it for you. It is really fun, educating, and interesting to read. You will also learn a whole lot about how you think, how others think, and how people think in groups. You will also understand how tricky it is to ask truly open and objective questions. Also, polls that seem objective are not due to the subjective structure of the question. I think you will enjoy this book, and I strongly recommend it.
The author tends to use nuclear weapon, war, and clinical examples more often than other topics in order to illustrate concepts. The examples are taken from actual empirical researches, including laboratory ones. Due to the purpose of the textbook, the examples are used to explain concepts, rather than to show how an experiment is designed or how "good" the experiment is in the sense of cause and effect. The bibliography list is correspondingly large given only some 260 pages. The author does not forget to provide tips on how to avoid particular biases presented in a given chapter. No exercises are provided at the end of each chapter, but a special section READER SURVEY given after preface asks you to answer 39 questions to be used in the main part of the text. No glossary is provided.
As I read through, I have warned myself not to generalize research results presented to be directly applicable to my life without careful thoughts. From my naïve point of view and based only on materials presented in the book, these research findings may be internally valid, but never guaranteed to work in any other circumstances or contexts. Such context dependence is treated explicitly in Chapter 4, but it all applies to any other concepts discussed throughout the book. The author warns this point to readers at the end of the book in Appendix. My suggestion for the next revision would be to include informal yet usable introduction on how to design an experiment that anyone could conduct without specialized devices or environment, so that readers can test biases that may be present in their own contexts.
As an example, having been an Amazon customer for a while, I see more votes on "yes" for reviews 4 and 5 stars than those on "no" for reviews 3 and below. The page is designed such that unaware people see most helpful review first, then most recent review in decreasing order by default. Your impression toward a book may change if you sort reviews by least helpful first or lowest rating first. Biases may be present in your purchase decision making processes. If you are curious about knowing some characterizations and explanations for them, this book may be for you.
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