In statistics we need to adjust confidence levels to significance of conclusions levels.

Sir, I refer to Tim Harford’s “Highs and lows of minimum wages”, March 28.

In it Harford writes: “A fascinating survey reported in the World Development Report showed World Bank staff some numbers and asked for an interpretation. In some cases, the staff was told that the data referred to the effectiveness of a skin cream; in other cases, they were told that the data were about whether minimum wages reduced poverty. The same numbers should lead to the same conclusions but staff had much more trouble drawing the statistically correct inference when they had been told the data were about minimum wages. It can be hard to set aside our preconceptions.”

Sorry, is that not good? Should World Bank staff not be more careful about drawing a statistically correct inference from data relating to skin cream than from data relating to something that could have such profound implications as minimum wages? In statistics, besides confidence levels, do we not need significance of conclusion levels?

For instance I sure wish that some staff somewhere, when presented data concerning the credit risk of bank borrowers, for the purpose of setting the equity requirements for banks, would have had the sufficient presence of mind to remind everyone of that what they really needed was data about what caused major bank failures… something completely different.

Had someone done so, and had someone been able to make bank regulators listen, the world would have saved itself many tears and much trouble.

@PerKurowski

PS. You might have noticed a made a slight mistake :-) I left it that way because, most probably, those who not understand what I really meant, might not be able to understand it even if I corrected it.