Statistical Analysis

Service Overview

Transform raw data into actionable insights through rigorous statistical analysis. Our statistical services provide comprehensive data analysis, hypothesis testing, predictive modeling, and experimental design to support evidence-based decision-making across research, business, and policy domains.

From exploratory data analysis to complex multivariate modeling and causal inference, we apply best-practice statistical methods to extract meaningful patterns, quantify uncertainty, and provide robust conclusions from your data.

Core Capabilities

• Exploratory Data Analysis: Data visualization, distribution analysis, outlier detection, and pattern identification
• Hypothesis Testing: t-tests, ANOVA, chi-square tests, non-parametric tests, multiple comparison corrections
• Regression Analysis: Linear, logistic, Poisson regression, mixed-effects models, and time series analysis
• Experimental Design: A/B testing, factorial designs, DOE (Design of Experiments), power analysis, and sample size calculations
• Bayesian Statistics: Bayesian inference, hierarchical models, MCMC methods, and probabilistic programming
• Causal Inference: Propensity score matching, instrumental variables, difference-in-differences, causal graphs

Best Practices & Methodologies

Statistical Rigor

• Assumption Validation: Check normality, homoscedasticity, independence, and other model assumptions
• Multiple Testing Corrections: Bonferroni, FDR, Holm-Bonferroni for controlling Type I error
• Effect Size Reporting: Report effect sizes (Cohen's d, r², odds ratios) alongside p-values
• Confidence Intervals: Always report uncertainty estimates and confidence intervals
• Reproducibility: Version-controlled analysis code, documented pipelines, and transparent reporting
• Model Validation: Cross-validation, holdout sets, residual diagnostics, and sensitivity analysis

Tools & Software

Industry Applications