درباره این اپیزود
What do diamond ring shopping, Uber pricing psychology, and active user metrics gone wrong have in common? They all highlight our complicated relationship with precision versus accuracy—and how that relationship can either build or destroy trust in our data. Arik Friedman from Atlassian joins us to unpack why being "about right" often beats being "exactly wrong," and why your nagging feeling that something's off might be a useful insight in and of itself. From the discipline of documenting assumptions to the art of knowing when to round your numbers, we tackle the very human challenge of working with data that's supposed to be objective but rarely is. Plus, we explore Twyman's Law (if data looks too good to be true, it probably is) and why sometimes your intuition is your last line of defense against embarrassing mistakes.
For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
