Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Confounding variables can create a false sense of sensitivity by altering the observed effect of a predictor.
B
Confounding variables have no impact on the sensitivity of predictors.
C
Sensitivity analysis is only concerned with independent variables, thus confounding variables are irrelevant.
D
All predictors will show increased sensitivity when confounding variables are present.
Understanding the Answer
Let's break down why this is correct
Answer
Confounding variables can make a predictor look more or less important than it truly is, because the confounder shares variation with both the predictor and the outcome. When you change the predictor in a sensitivity analysis, the effect you see may actually be due to the confounder rather than a direct causal influence. This means that the sensitivity you observe could be inflated or deflated, leading you to overestimate or underestimate the predictor’s real impact. For example, if smoking (confounder) is related to both air pollution (predictor) and lung disease (outcome), a sensitivity test that increases pollution may show a strong effect, but it might actually be driven by smoking. Thus, ignoring confounders can mislead the interpretation of how sensitive the outcome is to changes in the predictor.
Detailed Explanation
A confounding variable is a hidden factor that influences both a predictor and the outcome. Other options are incorrect because The idea that confounders do nothing is a misconception; Sensitivity analysis looks at how changes in any variable affect the outcome.
Key Concepts
sensitivity analysis
confounding variables
Topic
Sensitivity of Predictors
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In a statistical model, what does the threshold value represent in relation to the sensitivity of predictors?
easyComputer-science
Practice
2
Question 2In the context of predictive modeling, how does the sensitivity of a predictor relate to its specificity?
mediumComputer-science
Practice
3
Question 3In predictive modeling, which of the following best describes the relationship between specificity and statistical significance when evaluating the sensitivity of predictors?
hardComputer-science
Practice
4
Question 4Sensitivity of predictors : stability in predictions :: Responsiveness of a car : ?
easyComputer-science
Practice
5
Question 5How does the sensitivity of a predictor impact its generalization ability in machine learning?
easyComputer-science
Practice
6
Question 6Why is sensitivity of predictors important in machine learning models?
easyComputer-science
Practice
7
Question 7Which of the following statements about the sensitivity of predictors are true? Select all that apply.
hardComputer-science
Practice
8
Question 8A new predictor model is being developed. If the model shows significant changes in its predictions with slight variations in the input data, how would you classify its sensitivity?
easyComputer-science
Practice
Ready to Master More Topics?
Join thousands of students using Seekh's interactive learning platform to excel in their studies with personalized practice and detailed explanations.