Studies show that trees in neighborhoods where residents use leaf blowers more than 4 hours per week drop three times as many leaves and branches as trees in neighborhoods with less leaf blower usage. A lawn care company called "Rakes-R-Us" claims that home owners who want less leaves to drop in the fall should not use leaf blowers since it leads to more leaves falling on the ground.

Which common inferential error we discussed in class best describes the error that Rakes-R-Us is making?

Question

Studies show that trees in neighborhoods where residents use leaf blowers more than 4 hours per week drop three times as many leaves and branches as trees in neighborhoods with less leaf blower usage.  A lawn care company called "Rakes-R-Us" claims that home owners who want less leaves to drop in the fall should not use leaf blowers since it leads to more leaves falling on the ground.

Which common inferential error we discussed in class best describes the error that Rakes-R-Us is making?

BlackTom AI · Accepted Answer

Here is what the question is asking: identify the common inferential error in the situation where a lawn care company claims that using leaf blowers causes more leaves to fall, based on a study showing neighborhoods with higher leaf blower use have trees that shed more leaves and branches.

Option-related analysis (even though options are not provided, I’ll discuss the likely categories to evaluate):
- If an option were 'Correlation does not imply causation,' that would flag the general mistake of inferring one thing causes another from a relationship, which is relevant here but not as precise as addressing the direction of influence.
- If an option were 'Confounding variable,' that would point to the possibility that another factor drives both leaf blower use and leaf drop, such as tree species, age, or tree health, which could explain the association without a causal link from leaf blowers to leaf fall.
- If an option were 'Reverse causation,' that would claim the observed association is due to the outcome actually causing the presumed cause, i.e., more leaf fall leads to greater leaf blower use, rather than leaf blower use causing more leaves to fall.

In this scenario, the company’s claim asserts that using leaf blowers increases leaf fall in the trees, implying a causal direction from leaf blower use to leaf drop. However, the study showing higher leaf drop in neighborhoods with more leaf blower use could be explained by reverse causation: trees that shed more leaves naturally lead residents to use leaf blowers more often to tidy up, not the other way around. In other words, the observed association may reflect that increased leaf shedding triggers more cleanup activity, rather than cleanup activity causing more shedding.

Additionally, a traditional 'correlation does not imply causation' critique would be valid here, since a correlation between blower use and leaf drop does not automatically establish that one causes the other. The confounding variable explanation would also be plausible: there could be differences in tree species, climate, or neighborhood maintenance practices that influence both leaf shedding and blower usage.

Why reverse causation is the best fit (given the prompt’s framing): the claim attributes the cause (leaf blowers) to the effect (more leaves dropping). The more coherent interpretation of the observed pattern is that the effect (leaf fall) leads to more cleanup activity (leaf blower use), at least in part, rather than cleanups causing more shedding. This aligns with the idea of reverse causation rather than a straightforward causal link from blower use to leaf drop.

Bottom line: the error involves wrong causal direction, i.e., reverse causation. The alternative explanations (confounding, and general correlation-not-causation) are also relevant critiques, but the phrasing in the prompt aligns most closely with judging the causal direction as reversed from what the company claims.

FA25_QTM_110_1 Quiz #3: FPoCI and Selecting on the DV

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