Shubh Sinha Profile picture
Oct 20 13 tweets 2 min read Read on X
Data science is expanding rapidly but most practitioners skip the basics.

Cassie Kozyrkov (formerly Google's Chief Decision Scientist) previously shared 32 foundational questions that every data scientist should answer.

I got bored and distilled this into the 10 most helpful questions.

Each question offers multiple valid perspectives ⬇️Image
1/ Start with epistemology.

"What does it mean to know something?"

In data science, do we "know" something when our model predicts it? When p < 0.05? When we have correlations?
2/ Next: "How do you know that you know?"

For example, if a model has 95% accuracy, what does this really mean?
3/ Look into critical distinctions:

"What's the difference between knowledge, truth, assumptions, opinions, and beliefs?"
4/ The trust equation:

"What makes evidence trustworthy?"
5/ The daily dilemma:

"If logic and data point opposite directions, which way do you go?"

I try to avoid data paralysis where possible.
6/ The measurement trap:

"Does every quantity you've measured really exist?"
7/ The ML paradox:

"Can you make reliable predictions without understanding how something works?"
8/ The humility check:

"Are analytical insights facts?"
9/ The ethics question nobody asks:

"Is it ethical to use statistics for persuasion?"
10/ The builder's dilemma:

"Is there a difference between knowing something is true and acting as if it's true?
Healthcare is a segment where these questions meet reality.

In a way, there is an abundance of data but is largely underutilized (especially due to HIPAA compliance).

For us, @IntegralPrivacy focuses on "how do we unlock what we what already know" instead of just "what can we know?"

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