[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-201208-en":3,"doc-seo-201208-105":29,"detail-sidebar-cat-0-en-105":88},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},201208,8796096645457,"Arica Lee","https://ap-avatar.wpscdn.com/avatar/800003749518d68ffe3?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345340919836971",4,"Exam","CRAM Sheet - Argument Structure Words, Famous Flaws, Quantities & Inference","CRAM reference organizes logic and argument-analysis essentials for fast test preparation. It summarizes key argument structure words for identifying conclusions, evidence, author stance, concessions, and conditional reasoning, including contraposition and biconditionals. It lists common “famous flaws” such as necessary vs. sufficient reversals, sampling bias, ad hominem, false choices, equivocation, circularity, and self-contradiction. It also covers quantity term interpretations, negation-based transformations, assumption/objection families, strengthen/weaken/evaluate strategies, and parallel, principle-conform, and inference-structure matching approaches.","Argument Structure Words  \nCONC: so, thus, hence, therefore, consequently, this shows that, because of this, this demonstrates, conclude EVIDENCE: for, after all, because, since (F.A. B.S.)  \nAUTHOR: but, yet, however, should, clearly, obvi, probably CONCESSION: although, while, despite, even though  \nConditional Logic: Universals, Guarantees, Requirements GIVEN A → B X → ~Y and Z  \nCONTRAPOSITIVE: ~ B → ~A Y or ~Z → ~X  \nLEFT SIDE: if, when, the only, any, each, every, all  \nRIGHT SIDE: only if, only, ensures, guarantees, always, depends on, requires, must, then ( Req’d = Right side ) IF NOT: unless, without, until (negate what goes on Left)  \nBICONDITIONAL: if and only if, then and only then NEITHER/NOR: “not this and not that”  \nNESTED UNLESS: “If then A, then B, unless C”= A + ~C –> B  \nFamous Flaws  \nNecessary vs. Sufficient: reverses or negates a conditional Part vs. Whole: assumes a trait/property applies to both Causal: too sure of one possible explanation for something Unproven vs. Proven False: no ev / bad arg for X ≠ X false Sampling: sample is biased / too-small / unrepresentative Ad Hominem: dismiss view bc source is biased / hypocritical Inappropriate Appeals: emotion / opinion / dubious expert False Choice: treats 2 like the only 2 / assumes exclusivity Intent vs. Result: acts like all consequences are intended Equivocation: a word or concept is used two very diff ways Circular: premise restates or assumes the conclusion  \nSelf Contradiction: two of author’s claims butt heads  \nQuantity Terms  \nAt least one is = some, can, may, might, possible, could At least one isn’t = not all, not required, not sufficient At least a handful = many, often, commonly  \nMore than 50% = most, typically, usually, generally, tends to, likely, probably, few fail to be, almost all, nearly all Negative Translations: No A’s are B = All A’s are ~ B  \nFew A’s are B = Most A’s are ~ B Not all A’s are B’s = Some A’s are ~ B’s  \nInferences with “Most” Statements  \nMost A’s are B Most A’s are B Most A’s are B  \n+ Most A’s are C  + Most B’s are ~A   + Most B’s are C  \nSome B’s are C More B’s than A’s No inference  \n***ASSUMPTION / OBJECTION FAMILY***  \nIDing Assumptions: Conc has a new idea the Evid didn’t IDing Objections: Given the Evid is true, how could I still argue the Conc is wrong? (how can I argue the Anti-Conc?) Explain Curious Fact: (common on Str / Wkn /Flaw/ NA) Evidence describes a curious comparison, often a Correlation Conclusion proposes a Causal Explanation  \n-Think: Are there any Alternate Explanations? Reverse Causality? Both Effects of the same Cause?  \n-How Plausible is the Author's Explanation?  \nCause w/o Eff? Eff w/o Cause? No Cause, No Eff (control group)? Flaw (Very Predictable. 40% test a Famous Flaw)  \nIf no Famous Flaw, I D an Assumption or an Objection  \n-3 types of answers: Assumption, Objection, Description ASSUMPTION: takes for granted, presumes, fails to establish  \n-beware: strong/broad, use negation test (see below) if unsure OBJECTION: fails to consider, neglects/overlooks possibility  \n-if the overlooked thing was true, would it weaken the argument? DESCRIPTION: everything else  \n-Did the arg really do this? If so, is that really a problem?  \n-Infers from (evid) that (conc); Treats (evid) as though (conc)  \nNecessary Assumption ( Less Predictable, esp Q15-26)½ test a Missing Link Assumption, ½ Rule Out an Objection-Don’t see a Missing Link? Use Anti-Conclusion to I D an Obj.  \n-BEWARE strong/broad; EMBRACE weak, ruling-out,“not”  \n-NEGATION TEST: which answer, if negated, most weakens Sufficient Assumption (answer + evid → conclusion)  \n- “New Idea” in Conc connected to 1-mention Ev Idea  \n- Correct answer often contrapositive (CP) of prediction  \n- Wrong answers: right ideas, wrong order; lack new idea Principle-Strengthen (Very Predictable, Missing Link)  \n- “If [facts from Evid], then [Judgment from Conc]” or CP.  \n-Wrong Answers: different judgment, connect the judgment to unknowns/opposites instead o","cbCairftJvIOdwV0","https://ap.wps.com/l/cbCairftJvIOdwV0","pdf",124056,1,"English","en",105,"# Argument structure words\n## Evidence, conclusion, author stance\n## Conditional logic and contraposition\n# Famous flaws\n## Necessary vs. sufficient; part vs. whole\n## Causal, sampling, ad hominem, false choice\n## Equivocation, circularity, self-contradiction\n# Quantity terms and negation translations\n## Most/at least/few mappings\n## Inferring with “most” statements\n# Assumption, objection, and missing-link analysis\n## IDing assumptions and objections\n## Necessary vs. sufficient assumptions\n# Strengthen, weaken, evaluate (STR/WKN/EVAL)\n# Match families and inference families\n## Parallel, principle-conform, inference structure","[{\"question\":\"How do you identify conclusion, evidence, and author stance using argument-structure words?\",\"answer\":\"Use cue words: evidence markers like “for/because/since” support evidence, conclusion cues like “thus/therefore” indicate the conclusion, and stance/concession cues like “however/yet/although” reflect author attitude and concession.\"},{\"question\":\"What are the most common “famous flaws” and how can they be detected?\",\"answer\":\"Look for patterns such as necessary vs. sufficient reversal, part vs. whole errors, overly certain causal claims, biased or unrepresentative sampling, ad hominem attacks, false choice framing, equivocation, circular reasoning, and self-contradiction.\"},{\"question\":\"How do assumption/objection and missing-link questions work in this framework?\",\"answer\":\"Identify what the conclusion needs but the evidence doesn’t provide; then answer in the assumption/objection family. Use the missing-link idea and negate-test guidance to select which choice most weakens the argument when the overlooked piece is true.\"}]","CRAM Sheet - Argument Structure Words, Famous Flaws, Quantities & Inference | PDF",1788527234,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":83,"head_meta":85,"extra_data":87,"updated_unix":27},"cram-sheet-argument-structure-words-famous-flaws-quantities-inference","",{"@graph":35,"@context":82},[36,51,65],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/exam/",{"item":50,"name":13,"@type":42,"position":11},"https://docshare.wps.com/document/cram-sheet-argument-structure-words-famous-flaws-quantities-inference/201208/",{"url":50,"name":13,"@type":52,"author":53,"headline":13,"publisher":55,"fileFormat":58,"inLanguage":22,"description":14,"dateModified":59,"datePublished":59,"encodingFormat":58,"isAccessibleForFree":60,"interactionStatistic":61},"DigitalDocument",{"name":9,"@type":54},"Person",{"url":40,"name":56,"@type":57},"DocShare","Organization","application/pdf","2026-09-04",true,{"@type":62,"interactionType":63,"userInteractionCount":4},"InteractionCounter",{"@type":64},"ViewAction",{"@type":66,"mainEntity":67},"FAQPage",[68,74,78],{"name":69,"@type":70,"acceptedAnswer":71},"How do you identify conclusion, evidence, and author stance using argument-structure words?","Question",{"text":72,"@type":73},"Use cue words: evidence markers like “for/because/since” support evidence, conclusion cues like “thus/therefore” indicate the conclusion, and stance/concession cues like “however/yet/although” reflect author attitude and concession.","Answer",{"name":75,"@type":70,"acceptedAnswer":76},"What are the most common “famous flaws” and how can they be detected?",{"text":77,"@type":73},"Look for patterns such as necessary vs. sufficient reversal, part vs. whole errors, overly certain causal claims, biased or unrepresentative sampling, ad hominem attacks, false choice framing, equivocation, circular reasoning, and self-contradiction.",{"name":79,"@type":70,"acceptedAnswer":80},"How do assumption/objection and missing-link questions work in this framework?",{"text":81,"@type":73},"Identify what the conclusion needs but the evidence doesn’t provide; then answer in the assumption/objection family. Use the missing-link idea and negate-test guidance to select which choice most weakens the argument when the overlooked piece is true.","https://schema.org",{"og:url":50,"og:type":84,"og:title":13,"og:site_name":56,"og:description":14},"article",{"robots":86,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":89},[90,94,98,101,106,111,116,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":91,"show_sort_weight":92,"slug":93},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":95,"show_sort_weight":96,"slug":97},"Literature",80,"literature",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":99,"slug":100},70,"exam",{"id":102,"doc_module":4,"doc_module_name":45,"category_name":103,"show_sort_weight":104,"slug":105},5,"Comic",60,"comic",{"id":107,"doc_module":4,"doc_module_name":45,"category_name":108,"show_sort_weight":109,"slug":110},6,"Technology",50,"technology",{"id":112,"doc_module":4,"doc_module_name":45,"category_name":113,"show_sort_weight":114,"slug":115},7,"Healthcare",40,"healthcare",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":118,"show_sort_weight":119,"slug":120},8,"Research & Report",30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":102,"slug":136},19,"General","general"]