An art museum recently conducted a study analyzing visitor movement across its ten exhibition halls. The data revealed that visitors spent, on average, more than twice as long in galleries illuminated by natural overhead skylights as they did in galleries illuminated exclusively by artificial LED spotlighting. Based on this finding, the senior curator concluded that exposure to natural light directly causes museum visitors to develop a deeper appreciation for visual artwork.
Which of the following best describes the flaw in the senior curator's reasoning?
- AIt assumes without justification that artificial spotlighting has a physically damaging effect on delicate visual artwork.
- It fails to consider that alternative factors, such as the popularity or significance of the specific artworks displayed, could account for the difference in time visitors spent in the galleries.Answer
- CIt relies on empirical measurements of visitor viewing times that actively reinforce the curator's conclusion regarding natural light.
- DIt takes for granted that visitors who spend less time in artificially lit galleries inevitably harbor negative attitudes toward the displayed art.
- EIt draws a sweeping generalization about international museum architecture based on observations gathered from a single facility.
Answer
The argument's reasoning is flawed because it infers a direct causal link from a mere correlation, failing to consider that alternative factors—such as the fame or appeal of the artworks displayed in those specific galleries—could explain why visitors remained there longer.
The correct choice highlights the classic correlation-versus-causation fallacy present in the argument. The curator notices that naturally lit rooms coincide with longer visitor dwell times, but hastily attributes this outcome to the lighting itself while neglecting obvious confounding factors, such as whether those specific galleries contained higher-profile or larger paintings.
Step-by-Step Solution
Key Concept
Evaluating Reasoning Flaws: Correlation vs. Causation & Omitted Confounding Variables