statistical-analysis

Hypothesis testing on survey data involves selecting an appropriate significance test, evaluating p-values, and reporting effect sizes with confidence intervals. This skill guides test selection, handles missing data, and provides clear interpretations to support your decision making.

Trend analysis and outlier detection identify changes over time and flag unusual observations in business metrics. This skill applies descriptive statistics to extract distribution summaries, mean, median, and dispersion to reveal anomalies and patterns in your dataset.

Descriptive statistics summarize distributions using mean, median, mode, and dispersion while addressing missing data. This skill processes datasets to provide exploratory analysis, generating distribution summaries that highlight data completeness and central tendencies.

Statistical analysis works for scientific measurements, business metrics, and survey data. This skill applies exploratory analysis, significance testing, and anomaly detection across these domains, extracting actionable insights regardless of the data source.

Reporting confidence intervals and effect sizes requires calculating the magnitude of observed differences and their precision. This skill supports exploratory analysis by generating these statistical measures, ensuring results include practical context for decision making.