The related problem that I see actually more often is the "you don't have big data" problem. You know, in data science, you see people spending hours writing pandas scripts that replicate a few clicks in excel for a one of analysis. You see datasets of a few gigabytes being processed with spark when SQL would be fine. You see ML techniques being thrown at questions that could be answered simply and reliably with basi…
Be good-argument-driven, not data-driven
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Re: Be good-argument-driven, not data-driven
#42The related problem that I see actually more often is the "you don't have big data" problem. You know, in data science, you see people spending hours writing pandas scripts that replicate a few clicks in excel for a one of analysis. You see datasets of a few gigabytes being processed with spark when SQL would be fine. You see ML techniques being thrown at questions that could be answered simply and reliably with basi…
Re: Be good-argument-driven, not data-driven
#43Re: Be good-argument-driven, not data-driven
#44Re: Be good-argument-driven, not data-driven
#45A major exception to this reasoning is performance. Argument driven performance suggestions are wrong more than 80% of the time and likely wrong by several orders of magnitude. You can’t know just how wrong you are without appropriate data. This makes for a good litmus test of whether people are lying to you about software or, more likely, have absolutely no idea what they are doing.
Thus, the author would agree that in performance optimization, you should collect and analyze data.
Re: Be good-argument-driven, not data-driven
#46In this case the topic of value is the often fraught relationship between empiricism and rationalism, and the impacts each have on the scientific process, research, education, and how we go about understanding the world.
To operate with one with a complete absence of the other is to expose yourself to huge, often fundamental gaps in your thinking, your arguments, and your plans. This is what the author is ultimately getting at from the direction of the empirical: data, in the form of a large collection of discrete observations, can be used to justify a sea of mutually exclusive claims that may or may not be in accordance with reality, and that's to say nothing about the quality of the data itself.
Re: Be good-argument-driven, not data-driven
#47This reminds me of the Principal Chalmers meme. In this case, first pondering whether he is wrong, only to conclude that it's the data that's wrong. I know that's not what the article says per se, but it's only one slightly abstracted reinterpretation removed, as OP's title demonstrates.
Minor nit: Principal Skinner; Chalmers was the superintendent. https://www.knowyourmeme.com/memes/am-i-so-out-of-touch
Re: Be good-argument-driven, not data-driven
#48I 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
Re: Be good-argument-driven, not data-driven
#49"What you measure affects what you do. If you don't measure the right thing, you don't do the right thing." -- Joseph Stiglitz
Re: Be good-argument-driven, not data-driven
#50This reminds me a lot of the discussion of the scientific method by Karl Popper, and David Deutsch who was very influenced by Popper. "Being data-driven" sounds very empirical . Just look at the data, and see what you find in it. But you can't just let the data "speak for itself" without an explanation or a theory that interprets the data. Popper in Conjectures and Refutations : > Observation is always selective. It…
A single metric is just one very thin dimension from the temporal development of a complex process involving many factors. You need to watch a multitude of metrics to devise an explanative theory, and even then, that theory can be rendered flawed when new and unexpected factors come at play.