{"id":13767,"date":"2019-08-13T20:55:30","date_gmt":"2019-08-14T01:55:30","guid":{"rendered":"http:\/\/grockit.com\/blog\/gre\/?p=331"},"modified":"2020-09-11T20:41:25","modified_gmt":"2020-09-11T20:41:25","slug":"argument-writing-task","status":"publish","type":"post","link":"https:\/\/wpapp.kaptest.com\/study\/gre\/argument-writing-task\/","title":{"rendered":"GRE Argument Writing"},"content":{"rendered":"<p>For most test-takers, the Argument Task is a lot less frightening than the Issue Task. For one thing, it is a shorter task at 30 minutes, which means fewer words are expected. More importantly, though, the Argument Task relies less on outside evidence; your ability to recall an apt historical example or hypothetical situation&#8211;a rare and treasured skill for the Issue Task&#8211;is no longer necessary. All of your essay content is drawn straight from the prompt.<br \/>\nYour task is to critique the given argument in terms of logical soundness and strength of evidence. The given argument generally consists of a persuasive paragraph on some real-world issue. Don\u2019t worry; you won\u2019t be asked to pick apart the great rhetoricians; all of the argument paragraphs are carefully chosen for their logical flaws. You\u2019ll see arguments like \u201cAmericans should eat soy to prevent depression,\u201d \u201cThe Mozart School of Music is the best of its kind because of <em>x, y, <\/em>and <em>z,\u201d <\/em>and \u201cGrove College should preserve all-female education to improve morale among students and convince alumni to continue their support.\u201d Each argument will have its reasons, which may or may not appear logical on the surface; it is your task to find the flaws in their reasoning and present it clearly and persuasively.<br \/>\nThe GRE software will give you a few specific guidelines for critiquing the argument before you begin. These should not be ignored:<\/p>\n<ul>\n<li>You are not being asked to agree or disagree with any of the statements in the argument<\/li>\n<li>You should analyze the argument\u2019s line of reasoning<\/li>\n<li>You should consider questionable assumptions underlying the argument<\/li>\n<li>You should consider the extent to which the evidence presented supports the argument\u2019s conclusion<\/li>\n<li>You may discuss what additional evidence would help strengthen or refute the argument<\/li>\n<li>You may discuss what additional information, if any, would help you to evaluate the argument\u2019s conclusion.<\/li>\n<\/ul>\n<p>The above suggestions are extremely valuable to help guide your thinking. According to the guidelines, we should examine \u201cassumptions\u201d and the effectiveness or suitability of \u201cevidence;\u201d further, we should hypothesize what additional evidence could be used to strengthen or refute the argument. These three suggestions comprise a pretty reliable outline of the essay.<br \/>\nAnother important suggestion in the guidelines is that the Argument Task is <em>not <\/em>like the Issue Task for one key reason: you will <em>not <\/em>be asked to contribute your own opinion. If you encounter an argument advocating the consumption of soy to prevent depression, do not begin your essay by agreeing or disagreeing and providing evidence for your stance. Almost all the material for your writing is contained within the given argument.<br \/>\n&nbsp;<br \/>\n<div  style='padding-bottom:10px; ' class='av-special-heading av-special-heading-h3    avia-builder-el-0  el_before_av_iconlist  avia-builder-el-first  '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Argument Flaws<\/h3><div class='special-heading-border'><div class='special-heading-inner-border' ><\/div><\/div><\/div><br \/>\nBecause nearly all of your writing material comes from the given argument, you can imagine these arguments are not impenetrably persuasive. All arguments will contain multiple flaws and logical fallacies; some of those fallacies will come straight from that Intro to Logic class you might have taken in college (e.g. post hoc, fallacy of accident, etc). Lucky for us, we won\u2019t have to recall the fancy names of these fallacies&#8211;just being able to recognize them is good enough. Here\u2019s a quick overview of some of these flaws in plain English:<\/p>\n<ul>\n<li>Assuming that characteristics of a group apply to each member of that group<\/li>\n<li>Assuming that a certain condition is necessary for a certain outcome<\/li>\n<li>Drawing a weak analogy between two things<\/li>\n<li>Confusing a cause-effect relationship with a correlation (famously known as <em>post hoc ergo propter hoc,<\/em> i.e. correlation does not imply causation)<\/li>\n<li>Relying on inappropriate or potentially unrepresentative statistics<\/li>\n<li>Relying on biased or tainted data (methods for collecting data must be unbiased and the poll responses must be credible)<\/li>\n<\/ul>\n<p>Most of the arguments contain three or four of these flaws, making your body paragraph organization pretty simple. Becoming familiar with these flaws and how to spot them is the first step to writing a quality Argument Task. Let\u2019s look at these flaws in a little more depth:<br \/>\n<div  class='avia-icon-list-container   avia-builder-el-1  el_after_av_heading  el_before_av_sidebar '><ul class='avia-icon-list avia-icon-list-left av-iconlist-big avia_animate_when_almost_visible avia-iconlist-animate'>\n<li><div  class='iconlist_icon  avia-font-entypo-fontello'><span class='iconlist-char ' aria-hidden='true' data-av_icon='\ue816' data-av_iconfont='entypo-fontello'><\/span><\/div><article class=\"article-icon-entry \"  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='iconlist_content_wrap'><header class=\"entry-content-header\"><h4 class='av_iconlist_title iconlist_title   '  itemprop=\"headline\"  >The Member vs. Group Fallacy<\/h4><\/header><div class='iconlist_content  '  itemprop=\"text\"  ><p>It is pretty unrealistic to describe a group and then expect that every single member fulfills that characteristic. You can remember this fallacy by thinking about stereotypes. We generally think of stereotypes as harmful because they unfairly limit a certain group to one definable characteristic that is often founded on little to no evidence. In order to avoid the member-group fallacy, the argument should clearly state that a member is a representative of the group as a whole; most of the time, however, it won\u2019t.<\/p>\n<\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><div class='iconlist-timeline'><\/div><\/li>\n<li><div  class='iconlist_icon  avia-font-entypo-fontello'><span class='iconlist-char ' aria-hidden='true' data-av_icon='\ue816' data-av_iconfont='entypo-fontello'><\/span><\/div><article class=\"article-icon-entry \"  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='iconlist_content_wrap'><header class=\"entry-content-header\"><h4 class='av_iconlist_title iconlist_title   '  itemprop=\"headline\"  >The Necessary Condition Assumption<\/h4><\/header><div class='iconlist_content  '  itemprop=\"text\"  ><p>The speaker of an argument may assume that a certain course of action is <em>necessary<\/em> or <em>sufficient<\/em> to achieve a result. The \u201cnecessary\u201d line of reasoning is particularly weak if the speaker does not provide evidence that no other means of achieving the same result is possible. For example, a superintendent of a school argues that adopting a certain marketed reading program is necessary&#8211;i.e. the <em>only<\/em> means&#8211;to increase reading skills of students.<br \/>\nThe \u201csufficient\u201d line of reasoning is weak if the speaker fails to provide evidence that the proposed course of action would be sufficient to bring about the desired result by itself. In the above example, the superintendent may not have shown that the reading program by itself is enough to raise reading levels. There are other factors involved in this proposed outcome: preparedness of teachers and attentiveness of students.<\/p>\n<\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><div class='iconlist-timeline'><\/div><\/li>\n<li><div  class='iconlist_icon  avia-font-entypo-fontello'><span class='iconlist-char ' aria-hidden='true' data-av_icon='\ue816' data-av_iconfont='entypo-fontello'><\/span><\/div><article class=\"article-icon-entry \"  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='iconlist_content_wrap'><header class=\"entry-content-header\"><h4 class='av_iconlist_title iconlist_title   '  itemprop=\"headline\"  >Weak Analogies<\/h4><\/header><div class='iconlist_content  '  itemprop=\"text\"  ><p>The speaker may come to a conclusion about one thing on the basis of another thing. For example, if the manager of a business, say a trading card shop, may find that a big competitor in a different city has increased sales by moving from a downtown location to a suburban one. The argument may seem sound, but we can\u2019t completely analogize these different trading-card shops. First of all, the demographics in their respective cities may respond to different incentives. Maybe that particular city\u2019s downtown district was already on the rise, and the relocation merely reaped the benefits? Without this thorough background info, we can\u2019t make this analogy.<\/p>\n<\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><div class='iconlist-timeline'><\/div><\/li>\n<li><div  class='iconlist_icon  avia-font-entypo-fontello'><span class='iconlist-char ' aria-hidden='true' data-av_icon='\ue816' data-av_iconfont='entypo-fontello'><\/span><\/div><article class=\"article-icon-entry \"  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='iconlist_content_wrap'><header class=\"entry-content-header\"><h4 class='av_iconlist_title iconlist_title   '  itemprop=\"headline\"  >Correlation Does Not Imply Causation<\/h4><\/header><div class='iconlist_content  '  itemprop=\"text\"  ><p>This fallacy, more lovingly known as the post hoc fallacy, may be one of the most common you\u2019ll encounter when examining the pool of arguments, so it\u2019s essential that you master it. There are two basic ways a fallacious cause-and-effect claim can be made. First, the speaker may claim that a correlation suggests causation; just because two phenomena often occur together, it doesn\u2019t mean that one event causes the other. Second, the speaker may claim that a temporal relationship suggests causation; by the same logic, just because one event happens after another, it doesn\u2019t mean that event caused the other to occur.<br \/>\nA speaker may often use correlation to simply causation when a lurking variable is present. Take this argument for example: As ice cream sales increase, the rate of drowning deaths increases, so ice cream causes drowning. This one may take some head-scratching to realize that ice cream is more popular in the summer months, when water activities are also more popular.<\/p>\n<\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><div class='iconlist-timeline'><\/div><\/li>\n<li><div  class='iconlist_icon  avia-font-entypo-fontello'><span class='iconlist-char ' aria-hidden='true' data-av_icon='\ue816' data-av_iconfont='entypo-fontello'><\/span><\/div><article class=\"article-icon-entry \"  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='iconlist_content_wrap'><header class=\"entry-content-header\"><h4 class='av_iconlist_title iconlist_title   '  itemprop=\"headline\"  >Inappropriate Statistics<\/h4><\/header><div class='iconlist_content  '  itemprop=\"text\"  ><p>You will often find that these arguments cite statistical evidence to bolster their claims. As you may find out, simply citing evidence does not prove a claim since the statistics may be faulty, unrepresentative, or inapplicable. The speaker may often cite a statistic that polled a sample group in order to draw a conclusion about a larger group represented by the sample. This is where problems can arise. For a sample to adequately represent a larger population, it must be of significant size and characteristically representative of the population. For example, a speaker may try to make a broad claim about graduate school\u2019s impracticality by citing statistics from one particular university, e.g. 80 percent of University X undergrads were employed within one year of graduating, while only 50 percent of the graduate students of the same university were employed after one year. The statistics of one university simply cannot account for a sweeping claim about graduate education. To really identify the source of the employment disparity, we\u2019d have to compare the admission standards for undergrads and grad students, examine the economy of the surrounding area, compare the types of jobs sought by undergrads and grads, and show the distribution of majors among grads and undergrads.<\/p>\n<\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><div class='iconlist-timeline'><\/div><\/li>\n<li><div  class='iconlist_icon  avia-font-entypo-fontello'><span class='iconlist-char ' aria-hidden='true' data-av_icon='\ue816' data-av_iconfont='entypo-fontello'><\/span><\/div><article class=\"article-icon-entry \"  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='iconlist_content_wrap'><header class=\"entry-content-header\"><h4 class='av_iconlist_title iconlist_title   '  itemprop=\"headline\"  >Biased or Tainted Data<\/h4><\/header><div class='iconlist_content  '  itemprop=\"text\"  ><p>Tainted data is the second problem that could arise with data samples. For data to be considered legitimate it has to be collected in an unbiased, fair, and scientific manner, otherwise the quality of the data is compromised. For example, if there is reason to believe that survey responses are dishonest, the results may be unreliable. Further, the results may be unreliable if the method for collecting the data is biased, e.g. if the survey is designed, consciously or unconsciously, to yield certain responses. To spot tainted data, make sure that if a survey should be conducted anonymously&#8211;like in the workplace&#8211;then it is indicated. Also, watch out for surveys that try to manipulate responses by providing narrow options. For example, a survey asking the question \u201cWhat is your favorite ice cream flavor?\u201d should have more options than simply \u201ccoconut\u201d and \u201cmint;\u201d from those findings, we might fallaciously conclude that 78% of people identify \u201cmint\u201d as their favorite ice cream flavor.<\/p>\n<\/div><\/div><footer class=\"entry-footer\"><\/footer><\/article><div class='iconlist-timeline'><\/div><\/li>\n<\/ul><\/div><br \/>\nMost of the arguments in the <a href=\"https:\/\/www.ets.org\/gre\/revised_general\/prepare\/analytical_writing\/argument\/pool\">official argument question pool<\/a> contain more than one of these flaws. Understanding and recognizing these flaws is the key to top-notch writing on the Argument Task. We\u2019ll go in depth with these in the next installment.<\/p>\n<p class=\"p1\"><div  class='avia-builder-widget-area clearfix  avia-builder-el-2  el_after_av_iconlist  avia-builder-el-last '><div id=\"text-70\" class=\"widget clearfix widget_text\">\t\t\t<div class=\"textwidget\"><p><span data-sumome-listbuilder-embed-id=\"a78fe19e226d385662749ccaadcdccd7ecdcab651c77e3b874bfcb76a80605a7\"><\/span><\/p>\n<\/div>\n\t\t<\/div><div id=\"text-71\" class=\"widget clearfix widget_text\">\t\t\t<div class=\"textwidget\"><p><span data-sumome-listbuilder-embed-id=\"185e834399a9fdd414ded52f3f51a4735f464b8c612f006f44ffba835a649b4f\"><\/span><\/p>\n<\/div>\n\t\t<\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"<p>For most test-takers, the Argument Task is a lot less frightening than the Issue Task. For one thing, it is a shorter task at 30 minutes, which means fewer words are expected. More importantly, though, the Argument Task relies less on outside evidence; your ability to recall an apt historical example or hypothetical situation&#8211;a rare [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":27107,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[68],"tags":[299],"_links":{"self":[{"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/posts\/13767"}],"collection":[{"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/comments?post=13767"}],"version-history":[{"count":2,"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/posts\/13767\/revisions"}],"predecessor-version":[{"id":34904,"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/posts\/13767\/revisions\/34904"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/media\/27107"}],"wp:attachment":[{"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/media?parent=13767"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/categories?post=13767"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wpapp.kaptest.com\/study\/wp-json\/wp\/v2\/tags?post=13767"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}