How To Make The World Add Up by Tim Harford

 

How to make the world add up by Tim Harford: Book Review

If one happens to be in love with data and statistics, then this book is an unmissable read. Welcome to the world of numbers, data and statistics but from a different viewpoint. “How to make the world add up” is a book authored by Tim Harford that showcases ten rules that enables us to think differently about numbers, data and statistics.

As one reads this book, one thing that comes out is that the research to back this content was detailed and links events, situations and aspects that took place over the last hundred years. In certain parts of the book the reader may be confused as to the relevance of a particular example, real incident, research outcome to the topic, but as one continues to read forward in that section, the link that the author draws to those events with the base content of the book is enlightening. 

The basic premise the book concentrates on is the fact that even correct statistical claim can be deceiving and often not outlining the exact purpose of the statistics. It is here where the reader has to apply caution, pause and ask questions, important questions, relevant questions.

The first chapter is an interesting take on the book “How to lie with statistics” and concentrates on narrating to the reader how numbers and statistics can deceive you with even a correct data set and correct output of the statistics. “How to lie with statistics” is a book published in 1954 authored by American journalist, Darrell Huff, and is considered one of the most popular books to be published on statistics. The reader will enjoy the cynical take of the author on the book and also some interesting statistical results that had taken place over the years which the book “How to lie with Statistics” deals with.

The next part showcases the ten rules that the author recommends for the readers to use. The foremost one is about searching one’s feelings when one comes across a data or a statistical claim for the first time. The author focuses on the key question- “How does this make me feel?” when one comes across a statistical data for the first time. The key point for the readers is that of mastering one’s emotion and having a control on the same when reading a statistical data. The author takes the help of a real -life incident involving Abraham Bredius, an art critic and an art collector, and Van Meegeren, an alleged art forgery master to convey an interesting take on emotions. Through the stories of these two historical characters, the author showcases how emotions can ruin technical know -how and how emotions can put a cloud in front of actual events or for that sake, statistics.

The next rule emphasizes on pondering on one own’s personal experience vis-à-vis to that mentioned in the statistics. The author uses his own case scenario with respect to London bus and tube travel. The experience showcases that many a times what you experience and what you read about the same event may have a completely different outcome that is because there are a lot of permutations and combinations and assumptions related to the data or statistics that may not have been published ever.

The third rule concentrates on the fact that when one comes across a statistical claim one should first analyze as to what the claim actually means or what is the core purpose of the statistics to showcase. The golden question suggested – “Ask what is being counted?” In this section, the author uses certain statistical claim published in London over the years. Readers will find the author’s take on the wealthiest people statistics very entertaining and insightful.

The fourth rule concentrates on taking a long shot view of the statistical result. This part concentrates on the fact of comparison of statistical claim covering different period sets. The same statistical claim for a ten- year period, twenty -year period or a thirty -year period. It also suggests to concentrate on the scale. The golden question suggested – “Is that a big number?”

The fifth rule concentrates on the need to dig a little deeper into how a statistical output was produced – getting the back story. This part jumps through various research examples but actually gives the impression that it may not be actually practical to discover how a statistic was produced unless one has some sort of networking or connection in the right places. However, the book does mention the Cochrane library, an online data base of systematic research reviews which is helpful for understanding and having knowledge on randomized trials, though this would only be specific to a particular subject or line of topic.

The next rule showcases the readers to analyze what/ who is missing in the data set that is used in to draw a statistical claim. This point is a very crucial aspect to analyzing statistical claims. Big data sets used for statistical studies may not represent the actual population which may be the representative outcome of the study. The data size is not the most important criteria- the representative data sample is crucial. Small data sets representing the purpose of the study gives a far more reasonable outcome than a large/ big data set that covers everyone and everything. The gold questions suggested – “What numbers are and aren’t collected, what is and isn’t measured, inclusions and exclusions.

The seventh rule demands transparency especially in case of tech and big data. This section showcases, with some interesting examples, from big tech companies about how assumptions and big data can lead to a statistical claim that may not give the complete true picture. Here in the author also raises the differentiation in controls and scrutiny in case of big and small data and suggests that we should ask tough questions on accessibility of the data, assessment of the performance of the algorithm, expert evaluation of algorithm and their conclusion.

Eight rule concentrates on the concept of statistical bed rock and not to take the same for granted. Statistical bed rocks refer to the institutions that put hours, days and years together to bring the data and statistics to the public and the author showcases how public institutions are the back bone for any country’s decision-making process.

The ninth rule alerts the readers that misinformation can often come in beautiful designs and digital structures. This section shows the reader how in the modern information overload economy, beautifully designed charts, infographics and presentations may not actually give the correct statistical output representative of the actual scenario. However, correct base data sets added with good data visualization can get authentic results and impact. Here in, the author narrates the story of Florence Nightingale. This story is intriguing and show cases how the use of correct data mapping in adverse social conditions, helped save lives and improve the overall medical diagnosis.

The tenth rule suggested by the author is to keep an open mind when coming across a statistical claim or using statistical claims.

The book is a long read and will take time for the readers to cover individual chapters. Readers will enjoy detailed research on historical personalities connected with historical statistical events. The references to research studies and trials are insightful to read but may take time for mind to absorb.

The book is an enjoyable read for readers who love numbers, statistics and data. For the others, this would almost be like an adventure in the world of numbers and statistics and there is lot infotainment throughout the book.

An ideal book to keep it in your library and often revisit it.