Build an original worked example from explicit assumptions

What makes a worked example credible?

A credible example exposes its construction. The reader should be able to identify the teaching question, replace the inputs and reproduce the answer. Calling a scenario hypothetical belongs beside the calculation, where it prevents confusion, rather than in a footnote after a persuasive result.

Google Search Central recommends useful original material that resolves a reader’s task. The original contribution here is a transparent model and a decision boundary. It is not an invented customer story or a claim that a certain article format receives a ranking bonus. [Creating helpful, reliable, people-first content]

Choose a problem small enough to finish. “Improve paid acquisition” needs many unknowns. “Calculate how many additional accepted leads cover a proposed review cost” has a defined output. Keep the surrounding uncertainty visible without trying to solve the whole acquisition strategy inside one example.

A complete example: should a lead review be tested?

Illustrative scenario, USD: a team proposes an additional $240 review step for one completed lead cohort. Each incremental sale would contribute $120 after variable delivery costs. Its historical accepted-lead-to-sale rate is assumed to be 20% for this teaching model. None of these inputs describes a client or a market benchmark.

Expected contribution per additional accepted lead is $120 × 0.20 = $24. The arithmetic break-even point is $240 ÷ $24 = 10 additional accepted leads. This is an expectation, not a promise that exactly ten leads produce two sales. Sales outcomes are discrete, and the assumed rate may not hold for the newly accepted leads.

The source-backed measurement boundary also matters. Google Ads conversion actions can count one or every conversion after an interaction. Neither setting independently supplies an accepted-lead ledger. Before using an advertising count as the denominator, reconcile it with the business definition and remove duplicates. [Conversion counting options]

Which assumption could reverse the decision?

The acceptance decision depends strongly on the sale probability and contribution definition. Recalculate the threshold when either changes. Holding the $240 review cost constant, a 10% sale probability gives $12 expected contribution per accepted lead and a threshold of 20 additional accepted leads. At 30%, the threshold is approximately 6.67, before uncertainty or risk allowances.

Do not round a threshold down to make the proposal look easier. If a planning requirement must use whole leads, state the chosen ceiling and its reason. Preserve the unrounded calculation in the worksheet so another reviewer can separate mathematical rounding from a business safety margin.

Publish the evidence boundary with the result

• Put “illustrative scenario, USD” in the table caption and surrounding explanation.
• Attach a source to the platform rule, not to the invented $240 input.
• Keep the cost boundary, cohort maturity and definition of an accepted lead beside the formula.
• Record what data would replace each assumption before money is committed.
• Finish with a sensitivity check and a decision that the model can actually support.

I would publish the model as a way to frame a test, not as proof that the review should be purchased. A reader can now rebuild the threshold using their own costs. If the probability or incremental effect is unknown, that missing evidence becomes the next research task instead of disappearing behind a precise-looking number.

• Read the broader guide for this workflow
• Put the method into practice
• Review calculator result assumptions
• Trace a statistic to its source

Sources

AI-assisted writing and an AI-generated editorial cover. Source links and checked dates are listed below; illustrative examples are labeled in the text.

• Creating helpful, reliable, people-first content — checked 2026-10-18
• Conversion counting options — checked 2026-10-18