Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Higher specificity always leads to higher statistical significance.
B
Low specificity can result in misleading statistical significance and impact the sensitivity of predictors.
C
Statistical significance is unrelated to the sensitivity and specificity of predictors.
D
Increased sensitivity of predictors automatically improves specificity and statistical significance.
Understanding the Answer
Let's break down why this is correct
Answer
Specificity tells us how well a model correctly identifies negatives, while statistical significance tests whether the sensitivity we observe is unlikely to be due to chance. They are independent concepts: a predictor can have a high specificity but still have a sensitivity that is not statistically significant. The significance of sensitivity depends on the sample size, effect size, and variability, not on the specificity value. For example, a test might correctly reject 90 % of negatives (specificity 90 %) yet its ability to catch positives might be only slightly better than random, yielding a non‑significant sensitivity. Thus, high specificity does not guarantee that the sensitivity of a predictor is statistically significant.
Detailed Explanation
Low specificity means many false positives. Other options are incorrect because Higher specificity does not automatically raise statistical significance; Statistical significance is affected by specificity.
Key Concepts
predictive modeling
specificity
statistical significance
Topic
Sensitivity of Predictors
Difficulty
hard level question
Cognitive Level
understand
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