statistical-analysis

Anomaly detection in time series data uses z-score, IQR, and percentile methods to identify outliers. These statistical approaches flag data points deviating significantly from distributions, enabling investigation of operational anomalies.

A/B test significance checks on product metrics require hypothesis testing using t-tests or ANOVA. This determines whether observed differences between segments are meaningful by evaluating effect sizes, confidence intervals, and sample considerations.

Descriptive statistics for a numeric dataset are calculated using mean, median, percentiles, standard deviation, and IQR. These metrics summarize distributions to provide clear insights into business and operational data.

Use a t-test for hypothesis testing when comparing means between two segments, and a chi-squared test when evaluating relationships between categorical variables. Proper test selection depends on data format and distribution.

Moving average smoothing identifies trends in operational data by calculating averages across sliding time windows, like a 7-day period. This reduces noise in time series data to reveal underlying directional shifts.

Common pitfalls when interpreting statistical analysis results include confusing correlation with causation, ignoring multiple comparisons, and falling victim to Simpson's paradox or survivorship bias, leading to misinterpreted metric-driven decisions.