[allsunny]

I won’t belabor the point because others have already made it: this article assumes there is some way to sort through good and bad arguments in the absence of data - a pretty big leap. The reality is all of our arguments are appealing to some sort of data (eg previous experience), it’s just that it doesn’t always fit in a neat definition of data.

Obligatory: https://en.m.wikipedia.org/wiki/All_models_are_wrong

[ajkjk]

"Previous experience" is not what is meant by 'data' in this industry. If company's decision-making was including both data and experience/wisdom/intuition, it wouldn't be so frustratingly wrong all the time.

[allsunny]

I agree that's not what is meant by 'data' in the industry and I'm challenging that a little bit. However, even if we use the industry definition, what you're saying is hyperbole. Every company uses both data and experience to varying degrees. People get hung up when they think the balance isn't appropriate - not surprisingly, that happens when one or the other doesn't support their opinion. I'd rather be in a position of defending my opinion with data. It's already been quoted but... "If we have data, let's look at data. If all we have are opinions, let's go with mine."

[HPsquared]

There's lies, damn lies, and statistics. Models are further along, beyond statistics.

[allsunny]

Models are just applied statistics?