Question
Your experimentation platform sizes tests from a precomputed lookup table instead of a stats library. Each table row is [min_baseline_bps, max_baseline_bps, min_mde_bps, sample_size]: the row applies when the baseline conversion rate satisfies min_baseline_bps <= baseline_bps <= max_baseline_bps and the effect you want to detect satisfies mde_bps >= min_mde_bps. Given the table plus the experiment's baseline_bps and mde_bps, return the smallest sample_size among all applicable rows, or -1 when no row applies. Example: table = [[100, 500, 100, 20000], [100, 500, 200, 8000], [501, 1000, 100, 12000]], baseline_bps = 300, mde_bps = 250 returns 8000.
required_sample(table: list[list[int]], baseline_bps: int, mde_bps: int) → int[[[100,500,100,20000],[100,500,200,8000],[501,1000,100,12000]],300,250]out8000State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
Vibe coding: describe the solution in plain language (or narrate it) and the coach grades your approach. Generating runnable code from your description is coming next.