Picture a product review. A new entry point receives more clicks, but the share of people completing the full task does not move. One person sees a more discoverable entry; another sees extra activity that never reaches the intended outcome. The experiment table can support both statements.
The disagreement often begins before the results. One side wants to know whether people notice the entry point. The other cares about completion across the whole flow. A test described only as an attempt to improve conversion leaves room for both sides to claim the answer.
The experiment goal should support a decision
When I design a test, I turn the product idea into a hypothesis and state the decision the result is expected to inform. Clicks and completions may both belong in the analysis, but they do different jobs.
If the team is deciding whether to keep the entry point, the primary measure needs to show whether it creates the intended behavior. If the decision concerns the complete flow, downstream completion cannot be treated as a footnote.
The primary measure answers; the others show the trade-off
A primary measure gives the experiment one clear question. Supporting measures describe what else changed around it. Conflicting directions do not need to be compressed into a simple win or loss.
More clicks with unchanged completion may show that the entry is easier to find. It may also show that the extra interaction does not travel further. The experiment cannot choose the product priority for the team, but it can make that choice explicit.
Keep the original question in the review
Changing the primary measure after seeing the result turns the test into a different enquiry. A review should retain the original hypothesis, the chosen measure, and the product decision it was meant to support. Other metrics can then add context without replacing the question.
The conclusion may remain untidy: the entry point improved, while the full flow did not. The next decision still depends on the product goal. At least everyone is discussing the same trade-off instead of selecting a different column from the table.
A disagreement can shape the next test
Conflicting metrics can expose what the first test did not answer. A later experiment may narrow the change or move attention to the steps after the click. The useful hand-off is a clear account of the unresolved product question, without inventing a cleaner story for the current result.