I bought this book intending it to be a gift for a family member. We have a shared interest in statistics and this seemed like a good punt. Unfortunately it will not be passed on.
Firstly the positives:
The author is clearly very passionate about her field and has put together a truly awe inspiring set of statistics that completely support the stance that women are biased against in society. This clearly needs to change, and there are some very good examples of where this could be done easily and effectively. It has been an eye opener to me to consider some of the examples brought up, crash test dummies, drug testing and outdated sexist guidance in a number of areas.
The negatives:
There is a consistent pattern of stating a set of statistics, and then expressing an opinion that this is an example of gender bias which could be fixed with gender disaggregated data. On a first read the conclusions, which seem consistently that men make decisions and that those decisions are implicitly designed to make women worse off off, are not entirely supportable by the statistics gathered.
As an example, snow sweeping - which is carried out by clearing the main roads first and then minor roads. The statement made is that this is biased against women as this benefits those commuting by car (men) and harms (physically) those who travel on minor roads as pedestrian (women). The conclusion that data should have been gathered which included more women to prevent this bad decision.
Firstly, it could be plausible that the decision makers - being commuters by car themselves - might have made a decision based upon their experience. This is in and off itself is decoupled from gender. One could imagine a mixed panel of working men and working women making the same poor decision with equal gender representation.
Secondly, the bias that exists seems to be that men benefit more from the status quo than women due to the nature of the jobs they do (paid work, full time, greater male proportion). The imbalance here is not how streets are cleared but who the jobs are carried out by - so addressing gender bias via the job market would be a better path to removing the gender bias without addressing the poor decision making of how to clear snow.
Thirdly, if one were to gather gender dis-aggregated data this might incentivise equal gender participation. However, this does not guarantee that the set of people involved were from different selection groups and might still exclude those "people" that do not commute via main roads. I am sure plenty of women also benefited from the 'main road first' approach - so one could imagine a poll of opinions which covers 50/50 by gender but excludes non-commuters.
All of the above are independent on what the best way to clear snow is and what we even mean by "best" in this context (cheapest for execution, reducing road accidents, reducing hospital attendance).
I've already fixated on this one example too much, there are others but if you have read this far I risk boring you too much. The issues raised in the book could probably more accurately be expressed as a journey into poor decision making by excluding groups given selection bias. The biases are not specifically gender and do not generally indicate a bias against women (there are other biases and poor decision making at play that predominantly harm women as a side effect) - although there are also examples of terrible bias that deserve to be considered - crash test dummies for everyone please!!
I think the author sets off with an axe to grind, and spends the book grinding it. If you are feminist or pro-equality with a view that the world is biased there will be a lot to enjoy here. If you attack it objectively there are still some gems with regards to systemic bias but there are plenty of opinions that do not bear up to analysis.
Overall a good book (I think), just lacking a level of objective scientific rigor that would have made it's message more convincing.
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Customer reviews
Compelling, enlightening and entertaining data review!
Reviewed in the United Kingdom on 18 August 2019
The brilliance in this book is that Criado Perez presents multiple examples of a blatant male bias in the aggregate data being used to make decisions that affect women and men and leaves it sitting there for all to see that acceptance of the status quo is untenable, for any progressive and healthy society. There is no man-bashing here, thankfully. On the contrary, Caroline states that this gender bias is not malicious, it is and always has been unintentional, but it does disadvantage women in terms of vehicle safety, healthcare, and in myriad other ways. As a parent of three girls and two boys, several of whom are pursuing careers in STEM, this text is incredibly relevant to us. I could waffle on about the ways that this bias exists and that my offspring have encountered ad nauseam, from lab coats and goggles in only male sizes to consultants that think girls only exist to make coffee and look pretty. Enlightening, fascinating and mind-blowing data presented in an engaging and entertaining format - a very worthy 5 stars!I saw Caroline being interviewed at the Edinburgh book festival recently and was impressed by her candour about the online threats she has received simply by putting her head above the parapet to campaign to have just one woman represented on the new bank notes and also by her reticence in being perceived as a strident feminist. What she has written here is simply a factual representation but it has brought her into the line of fire from all kinds of misogynists and people who would wish to silence the voice of truth and reason. These entrenched biases are not going to be corrected overnight, despite how detrimental some of them are to us as women, but the more of us who tentatively stand up and say 'actually, we matter as well' then perhaps the more likely we are to finally be heard. Although in the current political climate - terrifyingly - the reverse might be true.
Reviewed in the United Kingdom on 18 August 2019
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