This guide helps you interpret outputs from the Sample Size (Proportion) without over-trusting a single number. You will learn what the tool assumes, which inputs matter most, and how to cross-check results against a second scenario. It is written for people who already ran the calculator and want a disciplined read of what the output implies for planning.
What this guide checks
- Whether the Sample Size (Proportion) output is plausible given your rough mental model.
- Whether the dominant inputs were verified against source documents, not memory.
- Whether you compared at least two scenarios (conservative vs optimistic).
- Whether the time basis (daily/monthly/yearly) matches how you think about the decision.
Signals that should trigger a second look
- Large swings in the Sample Size (Proportion) result when you nudge only one input by a realistic amount.
- Outputs that imply extreme leverage, negative durations, or impossible physical values.
- Mismatch between narrative expectations and the quantitative story the tool tells.
- Institutional constraints (policy caps, contractual floors) not represented in the model.
Common mistakes
- Entering proxy values because real data is inconvenient, then defending the output as precise.
- Mixing cohorts: blending historical averages with forward-looking targets without labeling them.
- Anchoring on a single KPI when the decision depends on a bundle of metrics.
- Stopping after the first acceptable result instead of recording assumptions for auditability.
Decision guidance
Low concern
If outputs move modestly when assumptions change and they align with back-of-envelope checks, confidence can be higher for directional planning.
Medium concern
If one or two inputs dominate sensitivity, treat the result as provisional until those drivers are validated or bounded.
High concern
If stakes are contractual, regulatory, or safety-critical, treat calculator output as a hypothesis and require independent verification.
Trust workflow (after you get a number)
- Save a screenshot or copy inputs/outputs with a timestamp into your working notes.
- Re-run with one conservative and one aggressive scenario and compare the spread.
- Open the linked Sample Size (Proportion) tool and confirm definitions in the on-page explanation.
- If the decision is material, align with finance, ops, or legal using the same definitions.