IC Percentil bootstrap

The Bootstrap Percentile Confidence Interval calculator estimates the range of values likely to contain a population parameter, using the 2.5% and 97.5% percentiles of a bootstrap-resampled dataset. This non-parametric method is ideal for small samples or when normal distribution assumptions are invalid. The 95% confidence interval reflects variability observed across repeated resamples of the original data.

It works by resampling the original dataset with replacement, computing the target statistic (e.g., mean) for each resample, and identifying the percentiles that form the interval. The simplified formula is CI = [2.5% percentile, 97.5% percentile]. This approach is robust to outliers and suitable for skewed data, though care is needed with the number of iterations to avoid inaccuracies.

Use this method in exploratory analyses, studies with non-normal datasets, or when parametric tools (like t/Z-based CIs) are limited. Practical examples include estimating average income in a group with extreme values or evaluating a drug's efficacy in a small clinical sample. Validity depends on the original sample's representativeness and the consistency of results across multiple runs.

Caution is required when working with datasets below 20 observations and ensuring sufficient bootstrap iterations (at least 1,000). The method may overestimate variability in highly skewed distributions. Interpret the interval within the problem context rather than focusing solely on numerical outputs.