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Roland: The alarming fact is that \(90\%\) of the people in this country now report that they know someone who is unemployed.
Sharon: But a normal, moderate level of unemployment is \(5\%\), with \(\frac{1}{20}\) workers unemployed. So at any given time if a person knows approximately \(50\) workers, \(1\) or more will very likely be unemployed.
Sharon's argument relies on the assumption that
| Text from Passage | Analysis |
| Roland: The alarming fact is that 90 percent of the people in this country now report that they know someone who is unemployed. |
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| Sharon: But a normal, moderate level of unemployment is 5 percent, with 1 out of 20 workers unemployed. |
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| So at any given time if a person knows approximately 50 workers, 1 or more will very likely be unemployed. |
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Roland presents a scary-sounding statistic, then Sharon responds by giving context about normal unemployment rates and showing how Roland's statistic is actually expected, not alarming.
Sharon concludes that knowing unemployed people is totally normal when you consider typical unemployment rates and how many workers people know.
Sharon uses basic math to show that Roland's alarming statistic is actually what we'd expect in a normal economy - if unemployment is 5% and people know about 50 workers, then of course most people will know someone unemployed.
Assumption - We need to find what Sharon must believe to be true for her argument to work. We're looking for unstated premises that Sharon's reasoning depends on.
Sharon makes specific quantitative claims: 5% unemployment rate, 1 out of 20 workers unemployed, and if someone knows 50 workers, 1 or more will likely be unemployed. Her argument connects statistical probability to Roland's 90% figure.
To find Sharon's assumptions, we need to identify gaps in her reasoning that could falsify her conclusion while keeping the stated facts intact. Sharon argues that knowing unemployed people is normal given the math, but what must be true for this mathematical connection to work?
This choice suggests Sharon assumes normal unemployment levels are rarely exceeded. However, Sharon's argument doesn't depend on how often unemployment exceeds normal levels. She's simply explaining that Roland's statistic is consistent with normal unemployment rates. Her mathematical reasoning works regardless of whether unemployment sometimes goes above \(5\%\). This isn't an assumption her argument requires.
This is the correct answer. Sharon's argument relies heavily on the idea that if \(5\%\) of workers are unemployed and people know about \(50\) workers, then most people will naturally know someone unemployed. But this mathematical connection only works if unemployment is distributed relatively evenly across different areas. If unemployment were concentrated in specific geographic regions, people in low-unemployment areas might know very few unemployed people, completely undermining Sharon's reasoning. Sharon must assume unemployment isn't geographically isolated for her statistical argument to hold.
This choice misrepresents Sharon's position. Sharon isn't arguing that \(90\%\) is always the normal percentage - she's arguing that \(90\%\) isn't alarming given normal unemployment rates. Her argument explains why Roland's \(90\%\) figure makes sense, not that it's always higher than \(90\%\). This assumption isn't necessary for her reasoning.
Sharon's argument doesn't depend on whether Roland is being honest about his statistics. She takes Roland's \(90\%\) figure at face value and explains why it's actually normal, not alarming. Even if Roland were distorting statistics, Sharon's mathematical reasoning about unemployment rates and personal networks would still be valid. This isn't a required assumption.
This choice deals with psychological effects of unemployment knowledge, but Sharon's argument is purely mathematical. She's not concerned with whether people feel more fear from personal knowledge versus statistics - she's simply showing that knowing unemployed people is statistically normal. Her reasoning doesn't require any assumptions about emotional responses to unemployment information.