RunScope library uses survey sampling to fix inaccurate progress bar ETAs

Progress bars commonly miscalculate estimated time remaining by extrapolating from early completed items, which fails badly when the heaviest work is concentrated later in a job. A developer tested this flaw across multiple workload types and found standard count-based ETAs can be off by 60 to 278 percent on uneven tasks. To fix this, they applied design-based survey sampling — the same statistical method used in ecology — to measure a small representative sample of remaining items before they run, producing an unbiased estimate with a real margin of error. This approach was packaged into a dependency-free Python library called RunScope, which reduced estimation error to near zero on the same hard workloads. RunScope works as a drop-in replacement for standard loops or tqdm and automatically selects the best estimation mode based on available data.