Data Detective: Ten Easy Rules to Make Sense of Statistics Tim Harford 2021 Riverhead Books

For those intimidated by math and anything mathematical, this book presents guidelines for readers to overcome mental phobias when confronted with statistical data. Filled with examples from literature of all types, Harford instructs the reader on how to approach statistics. Harford begins by attacking head-on the idea of lying with statistics, the antithesis of his book. Next with his first rule, he defined the basic mindset of any reviewer of statistics--one free of personal bias. The attempt to be objective is the first step for anyone who wants to evaluate the validity of statistical or any kind of data. Second, he advised that individuals understand the perspectives from which they view the numbers in front of them, from a close-up and personal vantage point or a distant and wider angle. As we have seen with the Covid-19 data worldwide, different countries record infection rates and death rates differently. Understanding the differences in methodology helps to avoid what Harford calls "premature enumeration" (p. 65). He used the example of the difference between miscarriages and premature deaths. How each gets defined depends on the country's health criteria. In the chapter, Step Back and Enjoy the View, Harford encouraged us to view statistical data in terms of the broader trends rather than accepting information in isolation. He made the comparison of "rolling business coverage of Bloomberg TV, the daily rhythm of the newspaper the Financial Times. . . and the weekly take of The Economist--each operates on unique information cycles. The fifth rule, "Get the Backstory" highlights the bias of what gets published in scientific journals and what gets omitted, the peer-reviewed "publication bias" . . .[i]nteresting findings are published; non-findings, or failures to replicate previous findings, face a higher publication hurdle" (p.113). The reader has the task of discerning the population sample and sample bias, "Ask Who Is Missing" constitutes the sixth rule. With the focus currently on the disaggregation of data, readers can target to which group the study applied to and to which group the data does not apply. Even the aspiration of N=All, an attempt for an all-inclusive population, will contain only those who conform to the criteria of inclusion. Harford concludes with one certainty. If algorithms are shown in a skewed sample of the world, they will reach a skewed conclusion" (p. 151). Continuing with the discussion of algorithms, Harford instructed the reader to ask the following questions when confronted with conclusions from big data: "Are the underlying data accessible? Has the performance of the algorithm been assessed rigorously--for example, by running a randomized trial to see if people make better decisions with or without algorithmic advice? Have independent experts been given a change to evaluate the algorithm? What have they concluded?" (p. 183). Within the chapter, he explained the difference between causation and correlation. "Figuring out what causes what is near-impossible, some say. Figuring out what is correlated with what is much cheaper and easier" (p. 156). Rule Eight, "Don't Take Statistical Bedrock for Granted", Harford warned that the data from such agencies as the Bureau of Economic Analysis, the Bureau of Labor Statistics, the Census Bureau, the Federal Reserve, the Department of Agriculture, and the Energy Information Administration provide the "nation's statistical bedrock" (p. 190). Here Harford defends the products of his profession. The last two chapters admonish us to delve carefully into the data and "Remember that Misinformation Can Be Beautiful Too" (p. 213) and that through all our investigation, we should keep an opened mind.

The Gray Rhino : How to Recognize and Act on the Obvious Dangers We Ignore by Michele Wucker

 After hearing Michele Wucker's TedTalk and long after I read The Black Swan: The Impact of the Highly Improbable, I decided to read her book on gray rhinos. She contends that many social phenomenon that receive the label of black swan actually come under the designation of gray rhinos.  As the title, The Black Swan suggests, events occur unexpectedly and without warning. Gray rhinos, in contrast, result in "highly obvious but ignored threats" (p. x) or respond insufficiently, weakly, and ineffectively. Among past "clear dangers that were recognized but weren't being addressed" (p. x), Wucker listed: climate change, financial crises, digital technologies, infrastructure failures, wildfires, water shortages, and others. 

To distinguish the types of events that individuals, organizations, agencies, governments and others face, Wucker categorized them according to four characteristics: low probability and high probability and low impact and high impact. Of the three types, white swans, black swans, and gray rhinos, she placed each into one of the four slots. White swans have a high probability of occurrence and low impact. In contrast, black swans and gray rhinos have high impact. However, black swans have low probability and gray rhinos have high probability. With any risky situation, the faster the response, the lower the cost.  

Leaders, who procrastinate when confronted with major challenges, ignore the opportunity or the avoidance of danger that the challenges offer. Wucker views this as the counterpart to danger. Assessing risk is inherent in the avoidance of danger or calamity. Each year at the World Economic Forum, assembled leaders in government, business, media, and Non-governmental Organizations prioritize what they consider the greatest risks in the Global Risks report. The United Nations conducts a similar survey. "In 2013, only 32 percent of those CEOs believed the economy was on track to meet the demands of a growing population within environmental and resource constraints, and just 33 percent believed that business was doing enough to meet those challenges" (p. 12). 

Behaviors that prevent action include many reactions: freezing and a lack of any response or denial and ignoring the threat. These reactions describe the first stage of the gray rhino response. The other four responses, according to Wucker, include" muddle along. . .come to an alert. . .play the blame game as we search for solutions. . .and, finally, we do something--occasionally before the trampling, but all too often after the fact" (p.27). These five responses constitute the five phases that most go through. To create a culture vigilant about gray rhinos or any of the other threats, "change perverse incentives in order to encourage leaders to act sooner, and uses out understanding of the weaknesses of human nature to make us more likely to do the right things" (p.27). In short, leaders thwart groupthink and encourage diverse and independent thinking. 

Project directors of the Good Judgment Project identified traits that separated a good forecaster from others: "First were psychological factors: 'inductive reasoning, pattern detection, open-mindedness and the tendency to look for information that goes against one's favored views, especially combined with political knowledge.' Second was the forecasting environment, including training in probabilistic reasoning and team discussion of rationales. Finally, not surprisingly, effort made a difference; the more time forecasters spent deliberating their predictions, the better they did" (p. 50).