MKT.10:4.4 - Estimate the supported effect and challenge its use

Give the identified question and qualified observations to an adequate estimation method. Retain the estimated magnitude, uncertainty and consequential assumptions. Statistical precision addresses variability under the method; it does not remove selection, measurement error, missing follow-up or a wrong causal model.

Check the conclusions that matter for the decision. Could plausible missing outcomes reverse it? Does a result disappear when an unsupported comparison is removed? Does an average conceal a group for whom the intervention is impractical or harmful? A subgroup claim needs adequate grounds; dividing the same small dataset repeatedly does not create them.

Keep discovery and confirmation distinct when many messages, channels or outcomes were examined. A selected favourable result may justify a new bounded comparison rather than immediate expansion. If a decision permits repeated inspection, agree how the resulting uncertainty will be handled with the analyst instead of repeatedly stopping at the first attractive number.

Before applying the result elsewhere, compare the new population, baseline service, intervention and support conditions. A trial with spare specialist capacity may not describe a larger programme that queues every customer. Establish whether the earlier estimate applies under those conditions, or obtain a new comparison. A precise result for the wrong use remains an inadequate answer.