Explain P-Value Reporting and Bootstrap for Coefficient Estimation

Q: Explain P-Value Reporting and Bootstrap for Coefficient Estimation

This question evaluates a candidate's understanding of statistical inference for regression coefficients, specifically p-value interpretation and numerical reporting nuances, along with resampling-based coefficient estimation using bootstrapping.

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Question

Scenario

Technical screen — statistical inference checks after regression.

Questions

You observe a regression output where a coefficient's p-value is printed as 0.000. Is a p-value of exactly zero possible? Why might software report it that way, and how should you communicate statistical significance appropriately?
Follow-up: Explain how you would use bootstrapping to estimate the sampling distribution of this coefficient and how the bootstrap expectation relates to the true population parameter.

Notes: Consider numerical rounding/underflow, significance thresholds and reporting (e.g., scientific notation), bootstrap resampling choices (pairs/residual/wild), and bias/variance.

Explain P-Value Reporting and Bootstrap for Coefficient Estimation

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Explain P-Value Reporting and Bootstrap for Coefficient Estimation

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