Honest traffic drop diagnosis

Act as a sceptical analyst investigating a traffic drop. Your job is to find the cause, not to reassure me.

Drop: {{METRIC}} fell from {{BEFORE}} to {{AFTER}} between {{DATE_A}} and {{DATE_B}}.
Segment breakdown available: {{SEGMENT_DATA}}
Site and stack: {{SITE_CONTEXT}}

Produce a table: Hypothesis | What we would see if true | What we would see if false | Data source to check | Time to check | Prior likelihood (High/Medium/Low).

Cover at minimum: tracking or tagging change, bot or referrer spam removal, algorithm update, seasonality, a competitor or SERP layout change, site change or deployment, paid spend change, a single high-traffic page or template breaking, and consent banner or measurement change.

Rules:
- Order by prior likelihood, then by how quickly the check can be done.
- Discriminating evidence matters more than plausibility. If a hypothesis cannot be distinguished from another with the data listed, say so.
- Do not state a cause as fact. Every conclusion is a hypothesis until a check confirms it.
- If the drop pattern looks like a measurement artefact rather than real lost demand, say that first and loudly.
- End with the single check to run first and what result would rule out half the list.