I started talking about Type I error rate and the interviewer immediately pushed on Type II error and sample size implications, which I hadn't fully connected yet in my head.
Frame the decision as a trade-off between Type I and Type II error costs, considering the business impact of false positives versus false negatives. Discuss how factors like sample size, effect size, and decision stakes influence the choice, and emphasize that 95% is a default but 99% may be warranted for high-risk changes. Conclude with a recommendation to align the confidence level with the product team's risk tolerance and the cost of errors.
Pro tip: Mention that at Google, many teams use 95% as a standard but will increase to 99% for changes that could significantly impact revenue or user trust, and always pre-register the confidence level to avoid p-hacking.
Understand the business goal, the metric being tested, and the potential impact of a false positive or false negative. Ask: What are the costs of shipping a bad change versus missing a good one?
Explain that a higher confidence level (99%) reduces Type I error (false positive) but increases Type II error (false negative) and requires a larger sample size or longer test duration to detect the same effect size.
Evaluate sample size availability, test duration, and the expected effect size. If the effect is small or the sample is limited, a 99% threshold may be impractical due to low power.
Reference company guidelines or team conventions. At Google, 95% is common, but for high-stakes changes (e.g., pricing, privacy), a 99% threshold might be preferred to minimize false positives.
Propose a confidence level based on the above factors, and emphasize the importance of pre-registering it to maintain statistical rigor and avoid post-hoc adjustments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the video context (e.g., platform, content type, time horizon) and defining the target metric (e.g., total views within 7 days). Then propose a structured forecasting approach that combines historical data analysis, feature engineering, and a predictive model, while acknowledging uncertainty and validation methods.
Pro tip: Emphasize that forecasting is iterative: start with a simple baseline (e.g., average views for similar videos) and then improve with more sophisticated models, always validating against a holdout set. This shows pragmatism and avoids overengineering.
Ask questions to understand the video type, platform, time frame, and what 'views' means (e.g., 3-second views vs. full watches). Define the forecast horizon and granularity.
Identify relevant data sources: historical video performance, metadata (title, tags, thumbnail), creator stats, and external factors (trends, seasonality). Clean and preprocess the data.
Create features like video age, creator subscriber count, past average views, time since upload, and content category. Choose a model (e.g., regression, time series, or gradient boosting) based on data size and interpretability needs.
Split data temporally (train on past, validate on recent). Train the model, evaluate with metrics like MAE or MAPE, and generate forecasts. Consider ensemble methods or uncertainty quantification.
Deploy the model, monitor performance, and update as new data arrives. Incorporate feedback loops and A/B tests to refine predictions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.