The Data Detective

Harford has written a pile of books on economics and ran a show decoding the world of statistics. I picked up The Data Detective: Ten Easy Rules to Make Sense of Statistics (2021) to see if it might be useful for undergraduates in the social sciences. Harford is a good storyteller, hence the pile of books, but the ten commandments and the golden role (be curious) are quite vague (essentially: avoid bias, use different data types, put data in context, know the source, question big data and algorithms, be open to change your mind). Useful, but not specific. At times the author speaks to fellow data nerds, however the majority of the content is introductory (too generic for university students). Well suited for a mass market book.

The book is in response to the "statistics lie" narrative, but a cautionary tale of how to engage them carefully and cautiously. The pitch: "I want to convince you that statistics can be used to illuminate reality with clarity and honesty. To do that, I need to show you that you can use statistical reasoning for yourself, sizing up the claims that surround you in the media, on social media, and in everyday conversation. I want to help you evaluate statistics from scratch, and just as important, to figure out where to find help that you can trust." (p. 9)

A few notes:

"The counterintuitive result is that presenting people with a detailed and balanced account of both sides of the argument may actually push people away from the center rather than pull them in. If we already have strong opinions, then we'll seize upon welcome evidence, but we'll find opposing data or arguments irritating. This biased assimilation of new evidence means that the more we know, the more partisan we're able to be on a fraught issue." (p. 36)

"A randomized controlled trial (RCT) is often described as the gold standard for medical evidence. In an RCT, some people receive the treatment being tested while others, chosen at random, are given either a placebo or the best known treatment. An RCT is indeed the fairest one-shot test of a new medical treatment, but if RCTs are subject to publication bias, we won't see the full picture of all the tests that have been done, and our conclusions are likely to be badly skewed." (p. 125-6)

"Modern data analytics can produce some miraculous results, but big data is often less trustworthy than small data. Small data can typically be scrutinized; big data tends to be locked away in the vaults of Silicon Valley. The simple statistical tools used to analyze small datasets are usually easy to check; pattern-recognizing algorithms can all too easily by mysterious and commercially sensitive black boxes." (p. 183).

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Hunting Causes and Using Them

We frequently read and use claims based on claims of causation. Yet, infrequently do we explore if the claims are well founded, or if the methods are well suited to the claims being made. Nancy Cartwright's "Hunting Causes and Using Them: Approaches in Philosophy and Economics" (2007) is a valuable resource to better engage with causation. The book, a collection of essays, "is for philosophers, economists and social scientists or for anyone who wants to understand what causality is, how to find out about it and what it is good for" (p. 1). Cartwright argues: "Our philosophical treatment of causation must make clear why the methods we use for testing causal claims provide good warrant for the uses to which we put those claims" (p. 2). 

The chapters cover a range of different topics and approaches to causation, however in general the book provides arguments for caution:

  • "What causes should be expected to do and how they do it - really, what causes are - can vary from one kind of system of causal relations to another and from case to case. Correlatively, so too will the methods for finding them... The important thing is that there is no single interesting characterizing feature of causation; hence no off-the-shelf or one-size-fits-all method for finding out about it, no 'gold standard' for judging causal relations" (p. 2). 
  • "Just as there is an untold variety of quantities that can be involved in laws, so too there is an untold variety of causal relations. Nature is rife with very specific causal relations involving these causal relations, laws that we represent most immediately using content-rich causal verbs: the pistons compress the air in the carbine chamber, the sun attracts the planets, the loss of skills among long-term unemployed workers discourages firms from opening new jobs... These are genuine facts, but more concrete than those reported in claims that use only the abstract vocabulary of 'cause' and 'prevent'. If we overlook this, we will lose a vast amount of information that we otherwise possess, important, useful information that can help us with crucial questions of design and control" (p. 19-20).
  • "There may be good evidence for the effectiveness of a policy conceived, as it usually is, in the abstract, but the actual outcomes may depend crucially on the find tuning of the method of implementation... Or consider poverty measures. Policy may set whether a poverty line should be relative or absolute and if relative, in what way (for instance, two-thirds of the median income). But the results - for instance, the poverty ranking among European countries - depend crucially on dozens and dozens of details of implementation (how to deal with individuals versus families, wealth or welfare benefits versus earned income, etc.), details where it seems that very different decisions can be equally motivated by the ranking will come out very differently depending on how these decisions are taken. The more the details matter, the more the problems of evidence multiply." (p. 41).
  • "I can summarize my view by comparing an economic model to a certain kind of ideal experiment in physics: criticizing economic models for using unrealistic assumptions is like criticizing Galileo's rolling ball experiments for using a plane honed to be as frictionless as possible. The defence of economic modelling has a bite, however. On the one hand, it makes clear why some kinds of unrealistic assumptions will do; but on the other, it highlights how totally misleading other kinds can be - and these other kinds of assumptions are ones that may be hard to avoid given the nature of contemporary economic theory." (p. 217)

Even if parts may be challenging for social scientists who are unfamiliar with economics and equations, the book is well worth reading.

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