Answer: Data used for bootstrapping must be resampled with replacement.

## Explanation Bootstrapping is a resampling technique used in statistics to estimate the sampling distribution of a statistic. The key characteristics of bootstrapping are: - **Resampling with replacement**: This is the fundamental principle of bootstrapping. The original dataset is resampled multiple times, with each sample drawn randomly from the original data WITH replacement, meaning the same observation can appear multiple times in a bootstrap sample. - **No distributional assumptions**: Unlike option A, bootstrapping does NOT require the data to follow any specific distribution (such as normal distribution). - **Empirical approach**: Bootstrapping works with the actual observed data rather than requiring knowledge of the underlying population parameters (contradicting option D). - **Limited outcomes**: Option B is incorrect because bootstrapping doesn't require all possible outcomes to be present; it works with the available sample data. In the context of option valuation, bootstrapping can be particularly useful when: - Limited historical data is available - The underlying distribution is unknown - Traditional parametric methods may not be appropriate Therefore, option C correctly identifies the core requirement of bootstrapping: resampling with replacement.

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