statistical-analysis

Statistical methods for descriptive analysis, trend detection, outlier identification, and hypothesis testing with practical business interpretation. 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...