Test the assumption that does the work.
Find out whether a conclusion survives a reasonable change to its most influential input.
A neat result can hide a fragile premise
A calculator can produce a precise answer from uncertain inputs. That precision is arithmetic, not a guarantee that the inputs describe the world. When a conclusion depends on a personal probability estimate, a displayed price and a cost assumption, the useful next question is not how many decimal places to show. It is which input makes the conclusion change.
Sensitivity analysis means varying an assumption while watching the result. You can use it to locate thresholds, compare plausible alternatives and identify what deserves more research. The exercise is particularly helpful when the story sounds convincing but you are unsure whether a modest adjustment would reverse the conclusion.
Change one input so you can see its effect
Consider a deliberately simple binary contract that costs forty-two cents and pays one dollar if YES resolves. For one hundred contracts with no fees, total entry cost is forty-two dollars. At an assumed sixty percent probability, expected net result is eighteen dollars. At forty percent, expected net result is negative two dollars. The contract has not changed; your belief has.
Moving probability alone makes the mechanism visible. Each percentage point changes this modeled expected result by one dollar because the possible total payout is one hundred dollars. That relationship holds for this particular quantity and payout definition. It does not mean a one-point revision has the same monetary effect in every instrument or every position size.
Locate the boundary, then inspect the neighborhood
With no fees, the break-even probability in this example is forty-two percent. At exactly that assumption, probability-weighted payout equals entry cost. If an entry fee is two percent of the purchase cost, total cost rises to forty-two dollars and eighty-four cents. Break-even probability rises to forty-two point eight four percent under the same simplified payout model.
A boundary helps you ask a more concrete question: is your estimate comfortably above it, close to it or plausibly on either side? You do not have to pretend your best estimate is exact. Try a lower, central and upper assumption that you can defend. Record why you chose those values rather than selecting a range that automatically preserves your preferred conclusion.
Do not let a tidy model erase omitted costs
The Oddscope stress test uses an entry-only percentage fee so that its arithmetic stays inspectable. A real venue can have a different fee schedule, quantity-dependent execution, exit costs or other contract-specific conditions. The stress test does not model all those details. Treat its output as an exploration of the stated assumptions and consult the original venue for the actual mechanics.
There is also a difference between a fixed displayed price and a price available for the entire quantity you have in mind. If you are studying a hypothetical example, say that the price is fixed by assumption. If you are examining a real observation, keep execution questions separate until you have the information needed to model them. Missing inputs should remain visible.
Turn a fragile result into a research plan
Suppose the sign of your expected result changes when probability moves by three percentage points. That is a useful finding even if you cannot immediately settle the estimate. Ask which evidence could distinguish the nearby assumptions. A clearer definition, a relevant historical comparison or a corrected source may matter more than another hour of general commentary about the topic.
Save the range, the threshold and the unanswered question together. When you revisit the study, you can compare a new observation with the assumption that actually mattered. Sensitivity work is not a machine for producing confidence. It is a way to direct attention: it shows where uncertainty affects the conclusion, where more detail has little effect and where an honest answer is still that you do not know enough.
You can keep a compact sensitivity table with three probability rows and two cost columns. Label every cell with its assumptions and include both realized outcomes alongside the expected result. The table makes it harder to focus only on the most attractive combination. It also lets a later reader see whether the conclusion holds across the range or depends on a single favorable corner.