Read the simulation rule before you read the percentile.

Produce one defensible interpretation sentence.

By the end, you should be able to say: “Under this stated simulation rule, this measured statistic varied across these generated realizations, with these important failure/tail observations.” That statement is narrower—and more useful—than “the 5th percentile predicts what happens next.”

Know what was randomized.

• The observed object or sample being simulated.
• The statistic recomputed on each realization.
• The resampling or generative rule.
• Any dependence, block-length, cost, or constraint assumptions.
• Whether the simulation was specified before or after candidate selection.

Read the result in this order.

1. Name the uncertainty. Sequence, sampling, parameter, model, or another explicitly defined source.
2. Name the rule. Independent resampling, block resampling, permutation, or a stated generative model.
3. Ask what the rule preserves. Identify ordering, dependence, regime, cost, or constraint information that remains—and what is destroyed.
4. Separate information from replicate count. Thousands of simulations can still come from a small or weak underlying sample.
5. Inspect the full distribution. Look at center, spread, tails, and failure regions rather than one favorable percentile.
6. Check selection timing. Ask whether the procedure and statistic were chosen before or after seeing candidate results.
7. Write the bounded conclusion. State what changed under the simulation rule and what remains unresolved.

Your conclusion should stay conditional.

The exact wording should reflect the actual procedure. If you cannot name the rule and the measured statistic, you do not yet have enough context to interpret the percentile.

Do not let more simulations create fake certainty.

• Treating the number of replicates as the amount of independent information.
• Calling a simulated percentile a future probability without a justified probabilistic model.
• Ignoring block length or other dependence assumptions.
• Showing only the favorable tail or summary.
• Running several simulation variants and reporting only the one that supports the preferred conclusion.
• Ignoring that the candidate itself was selected from a much larger search.

If the chart is impressive but the experiment is vague, stop at the question.

Ask for the missing rule, statistic, sample, and dependence assumptions. If those cannot be recovered, treat the graphic as incomplete evidence rather than filling the gaps with assumptions.

Watch the visual intuition after the written checklist.

Pair path uncertainty with evidence boundaries.