Question at position 9 True/False Question: MSE penalizes large errors more than MAE. TrueFalse

Question

Question at position 9  True/False Question: MSE penalizes large errors more than MAE. TrueFalse

BlackTom AI · Accepted Answer

When evaluating the statement about MSE and MAE, it’s helpful to recall what each metric does with errors.
Option 1: True. MSE stands for mean squared error, which squares each individual error before averaging. Squaring magnitudes emphasizes larger deviations more than smaller ones, so a single large error increases the metric more than a smaller error would. This makes MSE more sensitive to outliers and large mistakes.
Option 2: False. If we compare MAE, which is the mean absolute error, to MSE, MAE uses the absolute value of the errors and treats all deviations with equal weight regardless of scale. Therefore MAE does not penalize large errors as heavily as MSE does. The statement claiming MSE penalizes large errors more than MAE is essentially describing MSE’s defining property, not a counterexample.
Additional nuance: because MSE squares the errors, its units become squared relative to the original data, which can amplify the perceived impact of large errors even further, reinforcing its sensitivity to outliers. In contrast, MAE preserves the original units and grows linearly with error, which reduces the influence of extreme values compared to MSE.
In summary, the core idea is that squaring in MSE makes large errors count more than in MAE, whereas MAE averages absolute differences without such amplification.

Question at position 9 True/False Question: MSE penalizes large errors more than MAE. TrueFalse

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