Spot experiment readouts whose headline metric points the wrong way, and replace it with the right one

# Check whether each experiment readout uses a misleading metric Inputs: `requests/R1.json` ... `requests/R4.json`. Each is a readout of an A/B test with a `headline_metric` and a `headline_claim`. Conversion readouts give visitors and conversions per segment and variant. Order-value readouts give every order value per variant. Rules: - Conversion readouts: the headline metric is the pooled conversion rate (all conversions / all visitors per variant). The right metric is the segment-standardised rate: each variant's segment rates weighted by the segment's share of all visitors in the test (both variants together). The readout is misleading when the two metrics pick different winners; reason_code "simpson". - Order-value readouts: the headline metric is the mean order value. The right metric is the median order value (for an even count, the average of the two middle values). The readout is misleading when the two metrics pick different winners; reason_code "outlier". - If the readout is not misleading, reason_code is "none" and the right metric is still reported. - Rates are fractions rounded half-up to 4 decimals; medians are rounded half-up to the cent. - winner = the variant ("A" or "B") with the higher value on the right metric. Write `out/review.json`: `{"R1": {"misleading": <bool>, "reason_code": "simpson|outlier|none", "right_metric": "standardised_rate|median_order_value", "A": <value>, "B": <value>, "winner": "A|B"}, ...}` for all four. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.