Decide confidence level and forecast video views

## Part A — Choosing 95% vs 99% confidence level You are running an A/B test and must choose the confidence level for hypothesis testing / confidence intervals. **Question:** How would you decide between using a **95%** confidence level vs a **99%** confidence level? In your answer, address: - What business and statistical trade-offs change when moving from 95% to 99%? - How this affects **Type I error (false positives)**, **Type II error (false negatives)**, required **sample size**, and **time-to-decision**. - When you would prefer each choice (give realistic product scenarios). - Any adjustments you would consider for **multiple testing** (many metrics/segments) or sequential peeking. ## Part B — Predicting the number of video views You want to predict how many times a video will be watched. **Question:** Describe how you would forecast/predict **video watch counts (views)**. Please cover: - What exactly is the target (e.g., views in next 24 hours/7 days, lifetime views) and at what granularity (per video, per creator, per country)? - What data you would use (exposure/impressions, recommendations, follower graph, seasonality, content features, recency). - What modeling approach you would start with (simple baselines → more complex), how you would evaluate it (backtesting, metrics), and key failure modes (cold start, bots, non-stationarity, viral shocks).