analytical-thinking

Analytical Thinking

Clarity beats cleverness. End with an answer tied to structure and a stated confidence.

When to use

• Explaining or predicting a number: a metric moved, a cost is high, a funnel leaks.
• A messy question that needs a driver tree, hypotheses and a plan for what data to collect.
• Comparing concrete alternatives against criteria when the user wants the reasoning laid out.

Skip: open-ended idea generation with nothing to measure, or a short verdict with no decomposition asked for.

Before you start

1. State in one block: Analytical question (precise, ideally falsifiable) and Pass (Frame, Decompose, Hypotheses, Evidence, Synthesis).
2. Gather first. If you can query data, read dashboards, logs or code, do that before listing Evidence. Ask up to 3 questions only for definitions or data that tools cannot supply.
3. Light path. Small ask: a 2-level tree, 2 hypotheses, one observation each, answer in 3 lines.
4. If the user is choosing among concrete options, insert an Options matrix after Evidence: rows are options, columns are criteria (state any weights), cells are -, 0 or + with a one-line reason. Then Synthesis.

Steps

1. Frame

Question type (estimate, compare, explain, predict, optimize), unit of analysis, and baseline (even a hypothetical one).

2. Decompose

A tree or table of drivers or workstreams. Aim for branches that do not overlap and together cover the question well enough for the decision.

3. Hypotheses

Ranked H1, H2, H3. For each: what would we see if it were true, and what would falsify it?

4. Evidence

For each hypothesis: Observation: ... - Strength: one sentence on how much it supports or undermines the hypothesis and its main limit. Caveat: ...

No real data? Replace this step with a section titled Thought experiment (no data). Inputs you guess are shown as ranges and marked [ESTIMATED].

5. Synthesis

1. Answer to the analytical question.
2. Key uncertainty: the one unknown that swings the answer most.
3. Next data or step: what to collect or run next.

Pitfalls

• Mixing Hypotheses and Evidence in one list. Keep them separate.
• Reading a pattern from a few data points. State sample size or window.
• Ignoring base rates (how often this happens anyway) and confounders (what else changed).
• A single point estimate from guessed inputs. Use ranges and show which input moves the result most.
• A tree whose branches overlap, so the same cause is counted twice.

Worked example: references/example.md.

Checklist

• Question and Pass stated (Options matrix noted if used)
• Frame gives question type and baseline
• Decompose is scannable
• Hypotheses have falsifiers
• Evidence (or Thought experiment) maps to hypotheses; guesses marked [ESTIMATED]
• Synthesis: answer, key uncertainty, next step