from Hacker News

McNamara Fallacy

by sherilm on 4/17/24, 3:50 PM with 23 comments

  • by sp332 on 4/17/24, 4:19 PM

    A long, informed, and funny essay about this recently https://docseuss.medium.com/the-biggest-threat-facing-your-t...

  • by YouWhy on 4/17/24, 4:50 PM

    One of my acquaintances wrote this piece a couple of years back, asking if Meta/FB's use of product engagement metrics as a signal for IC performance was a case of the McNamara fallacy:

    https://4qbits.com/posts/2022-02-04-the-meta-fb-downturn-as-...

  • by smitty1e on 4/17/24, 8:23 PM

    You can't mention RSM without mentioning PPBS, which he begat and continues to bleed money at a Pentagon near you => https://en.m.wikipedia.org/wiki/Output_budgeting

  • by pflenker on 4/17/24, 4:07 PM

    I see that a lot in hiring. We can measure grades and experience, we can count diplomas or certifications, so we focus on these things and disregard soft skills like the ability to work with other people.

  • by moribvndvs on 4/17/24, 5:04 PM

    The technological society[0] in practice

    0 - https://archive.org/details/JacquesEllulTheTechnologicalSoci...

  • by swframe2 on 4/17/24, 4:09 PM

    I think more generally, the issue is that people want to believe lies.

  • by uoaei on 4/17/24, 4:06 PM

    This comes up constantly in debates around philosophy of science, particularly physics, but also the mind.

  • by dookahku on 4/17/24, 4:03 PM

    this reminds me of when Yahoo banned work from home in the late 2000s because they said the VPN data said they weren't working

  • by antisthenes on 4/17/24, 4:09 PM

    It's only a fallacy if you have garbage or incomplete data.

    Someone with poor reading comprehension will read this as "trusting the data over a more holistic approach", when the actual criticism would be something like "making decisions based on metrics than can be easily measured, rather than ALL metrics is a mistake".

    Generally speaking, if you have comprehensive and complete data and domain competence, it is not a mistake to make a decision based on objective metrics.