DA0-002 Data Analysis Practice Question
In a multiple regression model, one predictor has a high p-value (0.45). What should the analyst consider doing?
⚠ Common exam trap
Test-takers frequently confuse statistical insignificance with a need for data transformation or more data — candidates often assume any problematic predictor requires a technical fix rather than simple removal from the model.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove the predictor from the model
A p-value of 0.45 is far above the conventional significance threshold (typically 0.05), meaning there is no statistically significant evidence that this predictor is associated with the outcome variable after accounting for the other predictors. Removing non-significant predictors simplifies the model, reduces multicollinearity risk, and improves interpretability without meaningful loss of predictive power. This is the standard first step in backward elimination-style model refinement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Transform the predictor
Why it's wrong here
A high p-value of 0.45 indicates the predictor’s coefficient is not statistically significant, meaning it fails to reject the null hypothesis that the true coefficient is zero. Transforming the predictor would alter its functional form but does not address the underlying lack of a linear relationship with the response. This option is tempting because transformation is correctly used to linearise non-linear relationships or stabilise variance, scenarios where the predictor’s p-value would typically be low due to a genuine but mis-specified association.
- ✗
Keep the predictor regardless
Why it's wrong here
Retaining a predictor with p=0.45 keeps a term whose coefficient is indistinguishable from zero, inflating variance and complicating interpretation without improving fit. It is tempting because domain theory sometimes justifies keeping a variable, and would be correct when the predictor must remain for regulatory, contractual or known confounding reasons.
- ✓
Remove the predictor from the model
Why this is correct
A p-value of 0.45 exceeds typical significance thresholds, indicating the predictor's coefficient is not statistically distinguishable from zero given the other variables. Removing it simplifies the model and reduces multicollinearity, though the analyst should first check theoretical relevance.
- ✗
Increase the sample size
Why it's wrong here
Increasing sample size might reduce p-value but not necessarily; removing is more direct.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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