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    Given below are two statements - one is labelled as Assertion (A) and the other is labelled as Reason (R).Assertion (A): Sensitivity to targets reflec
    Question

    Given below are two statements - one is labelled as Assertion (A) and the other is labelled as Reason (R).
    Assertion (A): Sensitivity to targets reflects the placement of a flexible criterion and is measured in terms of 'hits minus false alarms'.
    Reasons (R): When the consequences of making a Miss are very grave, we lower the criterion for considering something as a Hit.
    In the light of above two statements choose the correct option:

    A.

    Both (A) and (R) are true and (R) is the correct explanation of (A)

    B.

    Both (A) and (R) are true and (R) is not the correct explanation of (A)

    C.

    (A) is true, but (R) is false

    D.

    (A) is false, but (R) is true

    Correct option is D

    Assertion (A) is false because in Signal Detection Theory (SDT), sensitivity (d') reflects discriminability between signal and noise, measured as the separation between signal and noise distributions, not simply "hits minus false alarms." The formula (hits - false alarms) represents response bias, not sensitivity. Reason (R) is true as it correctly describes criterion shift: when missing a signal has serious consequences (e.g., missing cancer in radiology), decision-makers adopt a more liberal criterion, lowering the threshold to classify stimuli as "signal present," thereby increasing hits while accepting more false alarms.

    Information Booster:
    ● Sensitivity (d'): Measure of perceptual/cognitive discriminability independent of response bias; calculated using z-score transformations of hit and false alarm rates
    ● Response bias (β or c): Reflects criterion placement; influenced by costs, benefits, and prior probabilities
    ● Criterion lowering: Increases hits but also increases false alarms; used when missing signals is costly
    ● ROC curves: Graphically represent the trade-off between hit rate and false alarm rate across different criteria
    ● Applications: Medical diagnosis, eyewitness testimony, quality control, threat detection
    ● SDT components: Distinguish perceptual ability (sensitivity) from decision strategy (bias)

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