Truth and Numbers

A very smart person remarked that he liked numbers because they didn’t lie. People lie about numbers.

Over the next 30 minutes, I demonstrated how two honest people can have two valid interpretations of the numbers, and have their models supported by the same facts.

An hour later, during our measurement science biweekly meeting, I invited the team to analyze a 5×5 RM table, and asked a fairly loaded question about it. Diversity in opinion eventually gave way to consensus around a mean. Several honest people had feedback and conflicting models about the way the world really worked. Each version perhaps more probably true than the last.

‘Truth’ is one of those really strange words in analytics. It’s something we all claim to have ownership to. Some believe that they have exclusive ownership to it. You’re entitled to your version of the truth. What you decide to do, however, with information that is contradictory to your position is the real differentiating behavior.

Successful people tend to have some sort of sustained competitive advantage. It involves the updating of current knowledge with new knowledge – much like sharpening a knife. If you’re willing to accept that your version of truth could be updated with new knowledge, and it improves accuracy of your decisions, then real progress is made. And if you defend your right to be curious and challenge other versions of truth, it should be done in a way where knowledge expands.

I believe that some people really do try to pull fast ones with numbers. People lie. Which is why if the evidence you know to be true doesn’t gel, then you’re free to challenge and discard. And that’s one of those more philosophical issues in analytics.

Proving something not to be true doesn’t automatically make it false. It just might as well be not proven. Psychologically – it makes engaging with others around data sets to be that much more rewarding.

Truth is out there. Each more predictably accurate than the last.