Due to a sudden, unexpected passing in the family a few years ago and another more recently and my subsequent (mental) health issues stemming from that, I was unable to download this book in time to review it before it was archived as I did not visit this site for several years after the bereavements. This meant I didn't read or venture onto netgalley for years as not only did it remind me of that person as they shared my passion for reading, but I also struggled to maintain interest in anything due to overwhelming depression. I was therefore unable to download this title in time and so I couldn't give a review as it wasn't successfully acquired before it was archived. The second issue that has happened with some of my other books is that I had them downloaded to one particular device and said device is now defunct, so I have no access to those books anymore, sadly.

This means I can't leave an accurate reflection of my feelings towards the book as I am unable to read it now and so I am leaving a message of explanation instead. I am now back to reading and reviewing full time as once considerable time had passed I have found that books have been helping me significantly in terms of my mindset and mental health - this was after having no interest in anything for quite a number of years after the passings. Anything requested and approved will be read and a review written and posted to Amazon (where I am a Hall of Famer & Top Reviewer), Goodreads (where I have several thousand friends and the same amount who follow my reviews) and Waterstones (or Barnes & Noble if the publisher is American based). Thank you for the opportunity and apologies for the inconvenience.

This book is a great introduction into the history and importance of data literacy. Alas, it has more of an academic tone and tended to focus more on historical than modern user cases. It probably doesn't have enough witty pop culture references, sassiness and snarkiness to become an airport bookstore staple or appeal to those readers who would benefit from reading it.

One of the most important aspects that seemed to be overlooked in the authors research is human laziness. We are incredibly lazy thanks to corporate capitalism and constant connectivity. We've been conditioned to react to sensationalizing and then forget about it a short while later when the next big news grabbing headline shakes us to our cores. And if we're always reacting, we're not applying critical thinking. I'm super curious how future generations going to overcome this, or is humanity doomed?

Recommended for: anyone who has heard something and thought, "wait a minute, that doesn't seem right" or rolled their eyes at every sensationalised news story.

Thanks to NetGalley and The Publisher for this eARC in exchange for an honest review.

The phrase, 'lies, damn lies and statistics' is often said and this book explains why you need to question the statistics presented to you.

This book is very apt for the current time, living through a global pandemic, where we are being bombarded with numbers and statistics on a continuous basis. Often, different arguments and interpretations are being put forward and it's hard to know who and what to trust. This book provides a good introduction for understanding all of this and what additional information or the types of questions you should be asking when numerical information is presented to you.

A quick and easy read that explains some complicated concepts clearly.

Personally as a numbers person I would have like a little more detail but I would easily recommend this book to those that need to get their head around understanding and interpreting the numbers that are presented to them on a daily basis.

This is my first post-Covid book, in as much as it has a foreword written in April 2020 which directly refers to the outbreak, and how in these times of mass statistical presentation, its contents are all the more vital. It is a translation of a Dutch book from 2018 which is basically a primer on the use and misuse of statistics, and is a broad overview - so perhaps a little more basic than I was expecting. There are excellent examples - some well known, some less so, about how to lie with statistics (and indeed How To Lie With Statistics by Darriel Huff gets more than a cursory mention).

Blauw has a very approachable style, and is interestingly on a bit of a journey through the book, starting from the viewpoint that if everyone knew more about statistic, they wouldn't be fooled so often, to realising that we all really just want our opinions confirmed, so knowing more, just allows us to lie to ourselves better using stats. But pleasingly because she is Dutch the examples are a little more wide ranging than the usual UK/US bias we get (it is still very very Western though - and the opening preamble which talks about her fieldwork in South America is admirably honest but displays all her Eurocentric biases). But Florence Nightingale gets a solid shout out, and there is plenty of time spent on Kinsey and the methodological errors of his grand sex survey but also so she can talk about his grand sex survey.

Its a brisk read, and I would have liked to have seen a bit more work on how statistic representation works - ie dodgy graphs. And if it was written now I imagine a section of Log Scales would have been appropriate, something that even a Meths graduate like me finds somewhat problematic depending on the type of data that is being presented. It is also a bit of a pity that the original title has been changed to the blander The Number Bias - the Dutch Title translates as The Bestselling Book In The World (With This Title) - which gives you a lot more of a flavour of the tone of the book. A good crash course on dodgy stats, and big data.