Question

Sheila: Health experts generally agree that smoking a tobacco product for many years is very likely to be harmful to the smoker's health. Tim: On the contrary, smoking has no effect on health at all: although my grandfather smoked three cigars a day from the age of fourteen, he died at age ninety-six. A major weakness of Tim's counterargument is that his counterargument (A) attempts to refute a probabilistic conclusion by claiming the existence of a single counterexample (B) challenges expert opinion on the basis of specific information unavailable to experts in the field (C) describes an individual case that is explicitly discounted as an exception to the experts' conclusion (D) presupposes that longevity and health status are unrelated to each other in the general population (E) tacitly assumes that those health experts who are in agreement on this issue arrived at that agreement independently of one another

Short answer

(A) attempts to refute a probabilistic conclusion by claiming the existence of a single counterexample.

Step-by-step solution

Understanding Sheila's Argument

Sheila's argument is based on expert consensus that smoking tobacco for years is likely harmful to health. This consensus is based on studies and statistical evidence suggesting a high probability that smoking is detrimental.

Understanding Tim's Counterargument

Tim counters Sheila's argument by citing a single example: his grandfather, who smoked three cigars daily yet lived to be 96 years old, apparently healthy. Tim uses this anecdote to claim smoking has no health effects.

Analyzing the Structure of Tim's Argument

Tim's argument attempts to disprove a broad probabilistic health claim with a singular anecdotal example. Probability statements mean that while most smokers are likely to experience health issues, there will be exceptions. Tim's singular example doesn't address the overall trend.

Evaluating the Argument's Weakness

The major weakness in Tim's argument is that he is attempting to refute the statistical and probabilistic nature of expert conclusions with an individual counterexample. This falls into the fallacy of hasty generalization.

Matching Weakness with Answer Choices

Among the options provided, choice (A) states that Tim tries to refute a probabilistic conclusion by presenting a single counterexample. This accurately describes the flaw in Tim's reasoning, as probabilistic conclusions are about likelihood, not certainties, and can tolerate exceptions.

Key concepts

Fallacy Analysis

Understanding logical fallacies is crucial for evaluating arguments effectively. A fallacy is a flaw or error in reasoning that weakens the argument. In Tim's case, we observe a specific fallacy known as a **hasty generalization**. When Tim argues that smoking is not harmful based on his grandfather's long life despite daily smoking, he commits this fallacy.

**Hasty Generalization**:
This occurs when someone draws a general conclusion from a small or unrepresentative sample of evidence. Rather than considering a wide range of data, Tim relies on a single anecdote. This is problematic because:

• Anecdotes don't consider broader statistical realities.
• They may ignore other influential factors, such as genetics or lifestyle.

By analyzing fallacies like hasty generalization, we learn that logical arguments need robust, representative evidence.

Probabilistic Reasoning

Probabilistic reasoning involves making conclusions based on the likelihood or probability of an event. In Sheila's case, her argument relies on expert consensus that smoking is likely harmful, based on extensive probabilistic data.

This reasoning acknowledges:

• Smoking increases the risk of health problems for most people.
• Exceptions exist, but they don't nullify the probability of the risk.

Probabilistic reasoning is essential because it helps evaluate risk and make informed decisions. Unlike absolute statements, it acknowledges uncertainty and variation, leading to more nuanced understanding.

Recognizing probabilistic statements allows individuals to discern between instances of certainty and likelihood, critical for making sound judgments.

Argument Evaluation

Evaluating an argument involves assessing its premises, evidence, and conclusion to judge its validity and soundness. Sheila's argument is grounded in expert consensus and broad statistical data, making it robust. Conversely, Tim uses a singular example, which:

• Fails to provide comprehensive evidence.
• Does not effectively challenge probabilistic conclusions.

For effective argument evaluation:

• Consider the **source** and **extent** of evidence. Is it robust and representative?
• Assess whether conclusions logically follow from premises.

Understanding these principles ensures you can determine when an argument is strong or weak, helping you form well-supported conclusions.

Counterarguments

A counterargument challenges the initial argument's claims. Tim presents a counterargument by citing his grandfather's anecdotal evidence against the harmful effects of smoking. However, effective counterarguments need to do more than offer simple anecdotes.

Criteria for Strong Counterarguments:

• Introduce **new evidence** or interpret existing data differently.
• Acknowledge the **robustness** of the original claim before critiquing it.

In academic and logical reasoning, counterarguments are valuable. They reveal potential oversights and refine the original argument. However, as exemplified, counterarguments must avoid logical fallacies to maintain credibility and contribute meaningfully to the discourse.

Expert Consensus

Expert consensus refers to the general agreement among specialists based on extensive data analysis and peer-reviewed research. Sheila's argument leans on such consensus to support the claim about smoking's health risks.

Importance of Expert Consensus:

• It is derived from comprehensive examination in a field, lending credibility.
• Enables laypeople to rely on informed judgments rather than personal anecdotes.

By understanding the role of expert consensus, individuals can better filter information and differentiate between scientifically supported claims and singular, unrepresentative counterexamples. Learning to trust expert consensus allows for decisions that are more aligned with scientific understanding.