Smart & Silly STEM: Science, Math, Tech, AI Quiz
Medium-difficulty multiple-choice quiz mixing facts, frameworks, and myth-busting across science, math, technology, and AI—with a wink.
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Quiz Questions & Answers
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Question 1: What's the best way to check if a model is overfitting?
Increase learning rate until validation improves
Compare performance on training versus held-out test data
Look only at training loss and stop when it reaches zero
Ensure the model has the most layers possible
Question 2: Which heuristic best captures scientific thinking?
Rely solely on intuition without experiments
Adopt the most popular theory without testing
Form testable hypotheses and try to falsify them
Collect only confirming data to build confidence
Question 3: Which approach best reduces bias in an experiment?
Only analyze participants who improved
Let participants choose their treatment
Use randomized controlled trials with blinding where possible
Increase sample size but keep allocation obvious
Question 4: In math modeling, why prefer simpler models when they perform similarly?
They require no validation on new data
They generalize better and are easier to interpret
They always have fewer parameters regardless of fit
Complexity guarantees future-proof predictions
Question 5: What's a common myth about AI that often causes unrealistic expectations?
All AI models are equally interpretable
AI can never assist with repetitive tasks
AI systems are fully objective and free of human bias
Simple rule-based systems are a form of AI bias
Question 6: Which practice best improves reproducibility of computational research?
Only publish final numbers without procedures
Use proprietary tools without documentation
Share code, data, and environment specifications alongside results
Describe methods only in high-level prose
Question 7: A robot uses sensor fusion to combine camera and lidar. What's the main benefit?
It always halves the computational cost
It makes each sensor unnecessary
It guarantees perfect perception in all weather
It increases robustness by leveraging complementary strengths
Question 8: When evaluating a tech claim, what's the highest-leverage first question to ask?
Who profits from the claim irrespective of data?
What evidence supports the claim and how was it measured?
Whether the claim sounds impressive on social media
If the claim uses the latest buzzword