What are Type I and Type II errors?
Assesses fundamental understanding of Data Science & Statistics conventions, runtime behavior, and memory/performance considerations.
Hiring managers look for precision, avoidance of ambiguous jargon, and ability to explain trade-offs under real production conditions.
A Type I error is a false positive: rejecting the null hypothesis when it is actually true. Its probability is the significance level alpha, usually 0.05. In a fraud system it means flagging a legitimate transaction.
A Type II error is a false negative: failing to reject the null when a real effect exists. Its probability is beta, and statistical power is 1 minus beta, the chance of detecting a true effect. In a medical test it means missing a real disease.
There is a tradeoff: lowering alpha reduces false positives but increases false negatives for a fixed sample size. Increasing sample size or effect size raises power.
from statsmodels.stats.power import TTestIndPower
n = TTestIndPower().solve_power(effect_size=0.2, power=0.8, alpha=0.05)
Choose alpha and power based on which error is more costly, and compute the sample size before the experiment.
Candidate Response Strategy & Interview Tips
- Start with a concise one-sentence summary: Deliver a direct, confident answer first before expanding into nuances.
- Demonstrate real-world trade-offs: Discuss where this approach excels and when you would avoid it in production systems.
- Discuss complexity & edge cases: Proactively explain time/space complexity or boundary conditions (null values, scale limits).
- Prepare for interviewer follow-ups: Technical hiring panels frequently probe deeper into concurrency, backward compatibility, or alternative libraries.