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).