Is claude-haiku-4.5-medium good at Data & Analytics?

“The model completely misses the primary statistical flaw (small-sample over-claiming). Instead of recognizing that n=8 is too small to trust the 50% difference (which could easily be noise or skewed by a single outlier), the model treats the 50% premium as a reliable fact. It then pivots to a completely different argument about unit economics versus total revenue, fabricating a hypothetical table to make its point. While the business questions it raises (CAC, LTV) are generally good, it fails the core analytical task of spotting the misleading statistic.”

“The model correctly identifies the intraday boundary problem and timestamp coercion. However, its first proposed solution (Option 1) uses BETWEEN with '2024-02-01', which is inclusive and will incorrectly include events occurring exactly at midnight on February 1st. The model falsely claims this captures up to Jan 31 23:59:59. While Option 2 is correct, recommending Option 1 as a preferred solution introduces a new date boundary error.”