{"id":1491,"date":"2021-08-05T15:34:15","date_gmt":"2021-08-05T15:34:15","guid":{"rendered":"https:\/\/www.terc.edu\/adultnumeracycenter\/?p=1491"},"modified":"2021-08-05T15:34:16","modified_gmt":"2021-08-05T15:34:16","slug":"special-summer-2021-edition-book-review-of-the-data-detective","status":"publish","type":"post","link":"https:\/\/www.terc.edu\/adultnumeracycenter\/special-summer-2021-edition-book-review-of-the-data-detective\/","title":{"rendered":"Special Summer 2021 Edition: Book Review of The Data Detective"},"content":{"rendered":"\n

by Melissa Braaten<\/p>\n\n\n\n

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There are three kinds of lies: lies, damned lies, and statistics.<\/em><\/p>
\u2013Everyone and no one<\/cite><\/blockquote>\n\n\n\n

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The quote above is so well-known that, like many popular sayings, it\u2019s been attributed to a number of different people, and we don\u2019t really know who used it first.1<\/a><\/sup> How do you respond to hearing that quote? In his new book, The Data Detective: Ten Easy Rules to Make Sense of Statistics<\/em>, Tim Harford attempts to unpack our cultural cynicism towards statistics and see if we can move towards a more productive attitude of curiosity and truth-seeking.<\/p>\n\n\n\n

I\u2019ve seen this mistrust toward data and statistics in quite a few teachers. Perhaps some of this mistrust comes from well-known problems with one of the most common forms of data collected in education: standardized testing. Perhaps it is the tedium of data collection and reporting that we have to do in our jobs, or our fear of reducing our students to numbers. Perhaps it is a symptom of a general cultural wariness of \u201cbig data\u201d; we know that data is being collected about us as we search and shop online and used in ways that are not always transparent. <\/p>\n\n\n\n

Or perhaps, as Harford suggests, it is a legacy of Darrell Huff\u2019s How to Lie with Statistics<\/em><\/a> \u2013 a best selling book, first published in 1954, that illustrates a number of common ways that statistics and data can be used to manipulate and mislead. In Harford\u2019s new book, he opens by examining his own history as a \u201cstatistics debunker.\u201d It was fun, but he has grown concerned. As a culture, Harford feels we have moved from a healthy, questioning skepticism towards statistics to a cynicism that dismisses the very real power they have to help make life or death decisions.\u00a0Harford uses the example of British researchers Richard Doll and Austin Bradford Hill (two of Huff\u2019s contemporaries) who were the first to use data and statistics to convincingly demonstrate that cigarettes cause lung cancer. Doll and Hill spent years collecting large sets of data to show that cigarette smoking made a person, statistically speaking, sixteen times<\/em> as likely to develop lung cancer. Can statistics be used to obscure the truth? Yes. But what Harford points out with this example is that they can also be used to reveal truths that might not be able to be seen any other way.<\/p>\n\n\n\n

What does responsible data skepticism look like? In the introduction to his book, Harford writes,<\/p>\n\n\n\n

\u201cIf we give in to a sense that we no longer have the power to figure out what\u2019s true, then we\u2019ve abandoned a vital tool. It\u2019s a tool that showed us that cigarettes are deadly. It\u2019s our only real chance of finding our way through the coronavirus crisis \u2013 or more broadly, understanding the complex world in which we live\u2026\u00a0Of course, we shouldn\u2019t be credulous \u2013 but the antidote to credulity isn\u2019t to believe nothing, but to have the confidence to assess information with curiosity and a healthy skepticism.\u201d<\/p><\/blockquote>\n\n\n\n

That certainly sounds like the type of data literacy I would like to have! It\u2019s the kind I\u2019d like to pass on to my students as well. <\/p>\n\n\n\n

Harford is a popular author, and he writes for the non-statistician. His book is full of current and historical anecdotes, rather than numbers, although he clearly explains how these events illustrate the larger point. He discusses the real and cultural phenomenon of  \u201cfake news.\u201d He uses the example of a successful art forger to show how our emotions can influence our interpretation of facts. He uses an example of how different hospitals recorded late miscarriages that affected our views of infant mortality. While his \u201cten easy rules to make sense of statistics\u201d are mostly things to watch out for, it is not because he thinks all the statistics out there are meant to mislead us.  Instead, they are habits of mind, or disciplines, to help us make the best use of a powerful and life-saving tool.<\/p>\n\n\n\n

Harford\u2019s Rule Two is to \u201cPonder Your Personal Experience.\u201d This stuck with me. In this chapter, he is contrasting two views of reality: what he calls the \u201cworm\u2019s-eye-view,\u201d which is the detailed, visceral understanding we have from our own experience, and the \u201cbird\u2019s-eye-view,\u201d the generalized, abstracted description offered by statistics. He illustrates the two views using the example of his experience riding the London subway system. Though his own commute is busy and uncomfortably crowded, published statistics claim that the average occupancy of the train is less than 130 people, which would mean only a handful of people in each car. How can he reconcile his experience with these numbers? Harford points out that both the worm- and the bird\u2019s-eye-view can be true at the same time, and together they provide a more complete picture of reality. As a commuter, he has a more intimate knowledge of the crowding on his <\/em>route, but he is not aware of the trains on the other side of the city that run almost empty. Looking only at the statistics, Harford\u2019s experience is lost.<\/p>\n\n\n\n

As a teacher, I have a worm\u2019s-eye-view of my students: day-by-day, detailed, relational knowledge about their learning, their lives, what seems to work and what doesn\u2019t. But I can also get lost if I assume that all other adult students are like mine. Many aren\u2019t. My program primarily serves immigrants, and our students tend to be older than average. We also specialize in students at early literacy levels. Other programs that serve recently out-of-school youth might find that their students\u2019 learning, lives, and needs are very different from those of mine. So while the statistics we collect about adult education students across the state encompass more data and variation, they can lose some of the detail and the human connection needed to think about how a policy might affect people. Both views might be correct, yet both have limitations. Harford makes a good case for bringing together data from different perspectives, both zoomed-in and zoomed-out, to help us have the best possible understanding of our complicated world.<\/p>\n\n\n\n

Admittedly, I like reading popular math books, so the fact that I found this an enjoyable read is not surprising. However, Harford\u2019s accessibility, and the fact that he uses so many current and relatable examples to illustrate his points, makes this a valuable book for anyone who is willing to examine their own preconceptions about data and statistics. This book could help teachers take a clear-eyed look at the role that data and statistics play in our lives, and value teaching them rigorously to help our students appreciate and use, rather than dismiss, these powerful tools.<\/p>\n\n\n\n

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[1]<\/sup> en.wikipedia.org<\/a><\/p>\n\n\n\n

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Melissa Braaten is an adult education instructor at Catholic Charities Haitian Multi-Services Center in Dorchester, MA. Melissa has taught ASE and pre-ASE math and reading, as well as ABE writing, computer skills, and health classes. Melissa also is a training and curriculum development specialist for the\u00a0SABES Mathematics and Adult Numeracy Curriculum & Instruction PD Center<\/a>\u00a0at\u00a0AV°ÍÊ¿<\/a>. She has written\u00a0several articles\u00a0for Math Musings, the Adult Numeracy blog.<\/em><\/p>\n<\/div><\/div>\n\n\n\n

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<\/div>","protected":false},"excerpt":{"rendered":"

by Melissa Braaten<\/p>\n

There are three kinds of lies: lies, damned lies, and statistics.<\/em>\u2013Everyone and no one<\/p>\n

The quote above is so well-known that, like many popular sayings, it\u2019s been attributed to a number of different people, and we don\u2019t really know who used it first.1<\/a> How do you respond to hearing that quote? In his new book, The Data Detective: Ten Easy Rules to Make Sense of Statistics<\/em>,  » Read more<\/a><\/p>\n","protected":false},"author":16,"featured_media":1492,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","footnotes":""},"categories":[8],"tags":[],"class_list":["post-1491","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-graphs-health-statistics"],"acf":[],"cp_meta_data":{"_edit_lock":["1628178252:16"],"_thumbnail_id":["1492"],"_edit_last":["16"],"custom_page_title":[""],"_custom_page_title":["field_5db45d9c2601b"],"external_link":[""],"_external_link":["field_5d6033845a92c"],"hide_share_buttons":["1"],"_hide_share_buttons":["field_5e5c1be61306c"],"meta_description":[""],"_meta_description":["field_60dd0445aa562"]},"_links":{"self":[{"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/posts\/1491","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/comments?post=1491"}],"version-history":[{"count":35,"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/posts\/1491\/revisions"}],"predecessor-version":[{"id":1527,"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/posts\/1491\/revisions\/1527"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/media\/1492"}],"wp:attachment":[{"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/media?parent=1491"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/categories?post=1491"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.terc.edu\/adultnumeracycenter\/wp-json\/wp\/v2\/tags?post=1491"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}