Question

A student is volunteering in a hospital with a stroke center. When asked what he believes is the prevalence of stroke among those greater than 65 years old, the student states that it is probably about \(40 \%\) even though data analysis indicates that it is significantly lower. What accounts for this error? (A) Deductive reasoning (B) Representativeness heuristic (C) Base rate fallacy (D) Confirmation bias

Short answer

C. Base rate fallacy

Step-by-step solution

Understand the Question

Identify what the question is asking. The student overestimates the prevalence of stroke among those over 65 years old despite data showing it is significantly lower. The task is to identify which reasoning error explains this overestimation.

Analyze Each Option

Consider each given option and how it might relate to the student's error: A. Deductive reasoning: This involves drawing specific conclusions from general statements. B. Representativeness heuristic: This is a mental shortcut where people judge probabilities based on how much something resembles a typical case. C. Base rate fallacy: This involves ignoring general statistical information (base rates) in favor of specific information. D. Confirmation bias: This refers to the tendency to search for or interpret information in a way that confirms one's preconceptions.

Match the Error to the Correct Concept

The student's overestimation of prevalence is a failure to consider the general statistical data (base rates). Instead, he may be influenced by specific cases he has encountered, leading him to ignore the broader data.

Conclude the Reasoning Error

Given that the student's error is due to ignoring the base rate information and focusing on individual cases, the most appropriate choice is C. Base rate fallacy.

Key concepts

deductive reasoning

Deductive reasoning is a method where conclusions are derived from general principles or premises. It’s like a top-down approach in logic. For example, if we know that all humans need water to survive and John is a human, we can deduce that John needs water to survive.

This type of reasoning guarantees that if the premises are true, the conclusion must be true. It's different from the error in the given problem. The error in the problem, which is overestimating the prevalence of strokes, isn't about drawing conclusions from general premises. It's more about using specific experiences or observations over statistical data.

representativeness heuristic

The representativeness heuristic is a mental shortcut. It makes probability judgments based on how much something resembles a typical case. Imagine you see a quiet, neat person who loves reading; you might assume they're a librarian because they match the 'representative' image of a librarian.

This heuristic can lead to errors. People might ignore actual statistical information in favor of how representative something seems. In the original problem, if the student overestimated stroke prevalence by thinking of typical stroke cases they have seen, this would be using the representativeness heuristic. But the actual error highlighted in the problem is about ignoring statistical data, so this isn't quite the correct reasoning error identified.

base rate fallacy

The base rate fallacy happens when people ignore general statistical information (base rates) in favor of specific information. Imagine you learn that a person is very tidy and think they are more likely to be a librarian than a salesperson, even though there are actually more salespeople than librarians. This is ignoring the base rate.

In the given problem, the student overestimates the stroke prevalence despite data indicating it's lower. This is because they may have ignored the hospital’s statistics (base rates) in favor of personal experiences or memorable cases they have seen. This is why the correct answer to the exercise is the base rate fallacy.

confirmation bias

Confirmation bias is when people search for, interpret, or remember information in a way that confirms their preconceptions. It’s like only seeing what you want to see. For example, if you believe eating a certain food causes headaches, you might only notice your headaches on the days you eat that food and ignore other potential causes.

This bias can lead to a skewed view of reality because it filters out evidence that contradicts one’s beliefs. In the exercise, if the student had a preconceived notion that strokes are very common, they might selectively focus on any information that confirms this and ignore what contradicts it. However, the problem identified the student’s error as more about ignoring statistical data, leading us to the base rate fallacy as the correct reasoning error.