Row 57670

Row ID: 57670 | Dataset Entry | Axioma AXP Content Repository

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I'm in a similar position at a mental health research lab. No outright manipulation of numbers to get desired conclusions but still negligent practices like garbagecan regressions, fishing expeditions, ignoring unexpected dataset row reduction after JOINs, what have you.

I just want to echo the dilemma of this being a common issue, which makes it hard to decide where to jump to when one wants to jump ship. It takes quite a toll, at least on me.

Today I was asked to look into LLMs, and essentially ignored when I said we shouldn't be blinded by the over-hype (ie LLM isnt what we need for causal inference, for example) and that we can't just keep throwing datasets with a lot of columns from our data warehouse without first addressing our lack of knowledge of the warehouse now that the dude that built it quit recently...these mfs really just view DA/DS as a monkey's job IMO...

Best of luck

FieldValue
text I'm in a similar position at a mental health research lab. No outright manipulation of numbers to get desired conclusions but still negligent practices like garbagecan regressions, fishing expeditions, ignoring unexpected dataset row reduction after JOINs, what have you. I just want to echo the dilemma of this being a common issue, which makes it hard to decide where to jump to when one wants to jump ship. It takes quite a toll, at least on me. Today I was asked to look into LLMs, and essentia…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-23
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{
  "text": "I'm in a similar position at a mental health research lab. No outright manipulation of numbers to get desired conclusions but still negligent practices like garbagecan regressions, fishing expeditions, ignoring unexpected dataset row reduction after JOINs, what have you.\n\nI just want to echo the dilemma of this being a common issue, which makes it hard to decide where to jump to when one wants to jump ship. It takes quite a toll, at least on me.\n\nToday I was asked to look into LLMs, and essentially ignored when I said we shouldn't be blinded by the over-hype (ie LLM isnt what we need for causal inference, for example) and that we can't just keep throwing datasets with a lot of columns from our data warehouse without first addressing our lack of knowledge of the warehouse now that the dude that built it quit recently...these mfs really just view DA/DS as a monkey's job IMO...\n\nBest of luck",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1YRTltUlV4dXR1SFhyWVBZZVV6OGZBcFJmdDZaTVVEZEhOR05qZVFTSjFWbks2X3VSS0t6R1E1NU5nV253QTBzUDUwR2lBMU5IdE93Qm01NXVLN3R1VXc9PQ==",
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}
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