I started listing things like selection bias and novelty effect, which was fine, but I could tell they wanted me to go deeper into why each one actually breaks the experiment rather than just naming them.
Start by defining what an experiment bias is and why it matters for product decisions. Then categorize biases into selection, measurement, and analysis types, giving a concrete example for each. Finally, emphasize how you would proactively mitigate these biases in Dropbox's experimentation process.
Pro tip: Mention that biases often compound, so a single mitigation like randomization isn't enough—you need a culture of experimentation hygiene, including pre-registration and blind analysis, to catch subtle issues.
Explain that biases are systematic errors that skew results. Categorize them into selection, measurement, and analysis biases to structure your answer.
Discuss biases like sampling bias, survivorship bias, and non-response bias. Give an example relevant to Dropbox, such as only testing on power users.
Cover biases like observer bias, instrument bias, and social desirability bias. Relate to how metrics are collected or how users behave differently when observed.
Include biases like confirmation bias, p-hacking, and cherry-picking. Emphasize how these can lead to false positives or overstated effects.
Suggest practical steps like randomization, blinding, pre-registration, and using holdout groups to reduce biases in experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.