How do I do percentile analysis (medians, p90, p95) with iDBQuery?

Percentiles describe your data at specific points in its distribution, the median (p50), the 90th percentile (p90), the 95th (p95), and they matter whenever the average hides the tail. iDBQuery computes them from plain language, no window-function SQL required.

• You'd ask iDBQuery: 'Show the median, p90, and p95 order-fulfilment time this quarter, by warehouse.'
• It computes each percentile per group, returns a chart plus a table, and contrasts them with the average.
• Every figure is cited to its source rows, so 'p95 = 6 days' can be verified against the actual slow orders in that tail.

Percentiles are how you manage experience, not just averages: a fast mean means little if one in twenty customers waits a week. Ask follow-ups in the same thread: 'Which warehouse has the worst p95?' or 'Show the orders above p95 so I can see what went wrong.' iDBQuery keeps the context and drills into the tail. Because it shows its SQL, an analyst can add p99 or change the grouping instantly. This is invaluable for SLAs, latency, response times, and pricing bands. Save it as a live report, and let the Analyst agent flag where the tail is widening even while the average holds steady, citing the rows. It works alongside distribution and SLA analysis.