{"id":33144,"date":"2021-04-22T11:05:33","date_gmt":"2021-04-22T15:05:33","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=33144"},"modified":"2021-04-24T14:18:10","modified_gmt":"2021-04-24T18:18:10","slug":"statistical-evaluation","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/statistical-evaluation\/","title":{"rendered":"Statistical Evaluation: How to Assess Legitimacy"},"content":{"rendered":"\n<p>What are the five <a href=\"https:\/\/www.shortform.com\/blog\/questions-you-should-ask-yourself\/\">questions you should ask yourself<\/a> when looking at statistics? How can you tell when a statistic is being manipulated?<\/p>\n\n\n\n<p>It is important to perform a statistical evaluation before you trust any statistic you&#8217;re given. Many statistics are manipulated to fulfill an agenda but even <a href=\"https:\/\/www.shortform.com\/blog\/manipulating-statistics\/\">manipulated statistics<\/a> can provide you with valuable information.<\/p>\n\n\n\n<p>Continue on to learn how to perform a statistical evaluation.<\/p>\n\n\n\n<!--more-->\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Statistical Evaluation Techniques<\/strong><\/h2>\n\n\n\n<p>In this article, you\u2019ll learn about a five-question checklist you can go through every time you encounter a statistic to do a statistical evaluation and assess its legitimacy. The goal is to find balance\u2014<strong>you don\u2019t want to swallow statistics without thinking about them<\/strong> (it\u2019s often worse to know something wrong than to be ignorant), <strong>but you also don\u2019t want to be so suspicious that you ignore <em>all<\/em> statistics and miss out on important information.<\/strong><\/p>\n\n\n\n<p>Here are the statistical evaluation questions:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Question #1: What Is the Source of the Figure?<\/strong><\/h3>\n\n\n\n<p>The first thing to do <strong>when confronted with a statistic is to figure out where it\u2019s coming from.<\/strong> The source may not be obvious, because liars often borrow the numbers of reputable organizations, such as universities or labs, but come to their own conclusions. Then, they try to make it look like <em>their <\/em>conclusion is the reputable organization&#8217;s conclusion. Always be suspicious of the phrase \u201cthe survey\/study shows\u201d; <em>who says <\/em>that the survey or study shows this?<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>For example, in an article about how women who attend college have a higher likelihood of becoming old maids, the writer cited data from Cornell about how many of its women students were married. Cornell <em>did <\/em>publish numbers about how many of its students were married, but the school didn\u2019t draw any conclusions. The conclusion in the article\u2014women who go to college are more likely to stay unmarried\u2014came from the article\u2019s <em>writer<\/em>, but since the data was from Cornell, it almost appeared as if the conclusion had come from <em>Cornell<\/em>.<\/li><\/ul>\n\n\n\n<p>Once you\u2019ve determined the source, look for these two <a href=\"https:\/\/www.shortform.com\/blog\/common-types-of-biases\/\">types of biases<\/a>:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><strong>Conscious.<\/strong> If you think the statistic is coming from a liar with an agenda, look for the techniques covered in the chapters above.&nbsp;<\/li><li><strong>Unconscious. <\/strong>If the bias is unconscious, there won\u2019t be obvious clues that the figures are inaccurate (for instance, the vague use of the word \u201caverage,\u201d without explaining which average they mean). If there are no signs of obvious lying, <strong>consider whether the source\u2019s agenda is furthered by the figures it gives and if this might have blinded them to certain ideas or further explorations of the data.<\/strong><\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Question #2: What Was the Data Collection Method?<\/strong><\/h3>\n\n\n\n<p>The second statistical evaluation question addresses the data collection method. Any data that\u2019s based on what respondents say, or how motivated they are to respond to something in a certain way, can skew the truth because people aren&#8217;t always truthful. <strong>When confronted with a statistic that was calculated based on people\u2019s responses, ask yourself if there\u2019s any reason the respondents might have been motivated to lie.<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>For example, one census in China, for military and tax purposes, found the population of one region to be 28 million. The next census, for famine relief purposes, found the population of the same region to be 105 million. The population hadn\u2019t changed much over the five years in between censuses\u2014people were just a lot keener to be counted when it meant famine relief than when it meant getting taxed.<\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Question #3: Is Any Relevant Information Omitted?<\/strong><\/h3>\n\n\n\n<p>In the third statistical evaluation question, you\u2019ll consider the context of the statistic. <strong>If a figure is cited on its own, ask yourself if any of the following accompanying information exists, and if leaving it out would further anyone\u2019s interests:<\/strong><\/p>\n\n\n\n<p><strong>1. Statistical qualifiers.<\/strong> See Chapter 2 for a discussion of what numbers need to accompany stats (such as degree of significance) to make them meaningful.<\/p>\n\n\n\n<p><strong>2. Other relevant figures.<\/strong> Consider what additional context statisticians would need to take into account to come up with the most accurate figure possible.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>For example, an environmentalist who wants the government to regulate pollution might cite a high death rate during pollution-related foggy weather in London and attribute the deaths to the fog. However, this doesn\u2019t represent how the world works\u2014people die for plenty of reasons that don\u2019t have anything to do with the weather, and the high death rate could have been caused by something else. A more accurate statistic would be to cite the death rate accompanied by cause of death: This would show how many people <em>truly<\/em> died due to fog.<\/li><\/ul>\n\n\n\n<p><strong>3. Cause.<\/strong> If an explanation for the figure isn\u2019t included, ask yourself what it might be. (If a liar leaves out the real cause, they can imply an effect was prompted by a more desirable cause.)&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>For example, one retail company wanted to show that business was improving because this year\u2019s April sales were better than last year\u2019s. A quick check of the calendar shows that Easter had fallen in March the year before and in April that year. Holiday sales were more likely responsible for the boost than an overall improvement in business.<\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Question #4: Is the Language Surrounding the Figures Misleading?<\/strong><\/h3>\n\n\n\n<p>Statistics are often reported in articles, surrounded by words (as opposed to in a table or chart). To answer this fourth question, <strong>study the words surrounding the figure and consider their definitions (to twist their results to suit their argument, liars may not use the most common definition of an everyday word, as you learned with \u201caverage\u201d).&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>(Shortform example #1: Anything can be the \u201cfirst\u201d or \u201cbiggest\u201d or \u201cbest\u201d of its kind, depending on how people define these words. For instance, the \u201cbiggest\u201d waterfall in Canada is Niagara Falls (if \u201cbig\u201d means the largest volume of water falling) or Della Falls (if \u201cbig\u201d means highest).)<\/li><li>Example #2: Accountants proposed using \u201cretained earnings\u201d or \u201cappreciation of fixed assets\u201d instead of the word \u201csurplus\u201d on corporate balance sheets. Most people know what surplus means, but not what the other words mean, so using these words in the balance sheets could hide how well companies were doing.<\/li><li>Example #3: In a statistical context, \u201cnormal\u201d doesn\u2019t mean \u201cideal\u201d or \u201cgood;\u201d it means \u201cusual.\u201d But since most people <em>do <\/em>associate \u201cnormal\u201d with \u201cgood,\u201d seeing the word paired with a statistic can leave them with inaccurate (and emotionally worrying) conclusions. For example, if you see a stat that says most children \u201cnormally\u201d start talking at a certain age and your child <em>doesn\u2019t <\/em>talk by that age, you might worry that she\u2019s abnormal or behind, when this isn\u2019t necessarily the case.<\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Question #5: Does that Figure Make Sense?<\/strong><\/h3>\n\n\n\n<p>To answer this last statistical evaluation question, <strong>don\u2019t blindly trust numbers\u2014consider if what they reveal actually makes sense.<\/strong><\/p>\n\n\n\n<p>There are four ways to assess a statistic against <a href=\"https:\/\/www.shortform.com\/blog\/common-sense-pamphlet\/\">common sense<\/a>:<\/p>\n\n\n\n<p><strong>1. Simply ask yourself if it seems right.<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>For example, according to the Rudolf Flesch readability formula, Plato\u2019s <em>Republic<\/em> was significantly easier to read than \u201cThe Legend of Sleepy Hollow.\u201d As <em>Republic <\/em>is a complicated, ethical dialogue written in 375 BC and \u201cSleepy Hollow\u201d is a short story, it doesn\u2019t seem right that the short story is more challenging to read.<\/li><\/ul>\n\n\n\n<p><strong>2. Compare the figure to commonly known and reputable facts.<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>For example, one urologist calculated that there are eight million cases of prostate cancer in the US. At the time, the male population of the US was less than eight million, which meant the figure couldn\u2019t be accurate.<\/li><\/ul>\n\n\n\n<p><strong>3. Consider the figure\u2019s precision. <\/strong>If the figure represents something abstract or difficult to measure (such as happiness), then it\u2019s unlikely someone would have actually been able to measure it with a decimal point\u2019s worth of precision.<\/p>\n\n\n\n<p><strong>4. Remember that extrapolation has limits. <\/strong>If a figure is based on extrapolation, be mindful that extrapolations are nothing more than educated guesses. Often, reality turns out to be much different from what the extrapolation predicted because things never continue to grow as you expect.&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>For example, based on population data from 1790 to 1860, Abraham Lincoln predicted that the country\u2019s population would be over 150 million by 1930, which was very incorrect.<\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"<p>What are the five questions you should ask yourself when looking at statistics? How can you tell when a statistic is being manipulated? It is important to perform a statistical evaluation before you trust any statistic you&#8217;re given. Many statistics are manipulated to fulfill an agenda but even manipulated statistics can provide you with valuable information. Continue on to learn how to perform a statistical evaluation.<\/p>\n","protected":false},"author":8,"featured_media":33250,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[34,15,25],"tags":[268],"class_list":["post-33144","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-communication","category-education","category-statistics","tag-how-to-lie-with-statistics","","tg-column-two"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.3 (Yoast SEO v24.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Statistical Evaluation: How to Assess Legitimacy - Shortform Books<\/title>\n<meta name=\"description\" content=\"It&#039;s important to do a statistical evaluation before you trust any statistic. 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