statistical-analysis

Descriptive statistics, trend analysis, outlier detection, hypothesis testing, and guidance on when to be cautious about statistical claims. Choose the right measure of center based on the data: Always report mean and median together for business metrics. If they diverge significantly, the data is skewed and the mean alone is misleading. Report key percentiles to tell a richer story than mean alone: Example narrative : "The median session duration is 4.2 minutes, but the top 10% of users spend over 22 minutes per session, pulling the mean up to 7.8 minutes." Characterize every numeric distribution you analyze: Moving averages to smooth noise: Period-over-period comparison : Growth rates : Check for periodic patterns: For business analysts (not data scientists), use straightforward methods: Always communicate uncertainty . Provide a range, not a point estimate: When to escalate to a data scientist : Non-linear trends, multiple seasonalities, external factors (marketing spend, holidays), or when forecast accuracy matters for resource allocation. Z-score method (for normally distributed data): IQR method (robust to non-normal distributions): Percentile method (simplest): Do NOT automatically remove outliers. Instead: Report what you did : "We excluded 47 records (0.3%) with transaction amounts >$50K, which represent bulk enterprise orders analyzed separately." For detecting...