Statistical Analysis & Research
Data-Driven Decisions With Statistical Rigor
Transform uncertainty into confidence with rigorous statistical analysis. From experimental design and hypothesis testing to time series forecasting and causal inference, we apply proven methodologies to answer your most important questions.
What is Statistical Analysis?
Scientific rigor for business decisions
Statistical analysis applies mathematical methods to data to identify patterns, test hypotheses, and quantify uncertainty. Unlike descriptive analytics that simply reports what happened, statistical analysis helps you understand why things happen and predict what will happen with measured confidence.
Our statistical analysis services cover the full spectrum of analytical needs: descriptive statistics to summarize data, inferential statistics to draw conclusions about populations from samples, hypothesis testing to validate business assumptions, experimental design for A/B testing, time series analysis for forecasting, and advanced multivariate methods for complex relationships.
We bring scientific rigor to business decisions. Every analysis includes proper methodology selection, assumption validation, confidence intervals, and effect size estimation. You get not just an answer, but an understanding of how confident you can be in that answer and what factors might change it.
Key Metrics
Why Choose DevSimplex for Statistical Analysis?
Research-grade methodology with business focus
We have completed over 150 statistical research projects and run more than 500 A/B tests, supporting publications in peer-reviewed journals and driving millions of dollars in optimized business decisions. Our team includes statisticians with advanced degrees and industry experience.
Our approach combines academic rigor with practical business sense. We do not just run tests - we design experiments that answer the right questions, select appropriate methodologies, validate assumptions, and interpret results in business context. We know that statistical significance is not the same as practical significance.
We are experienced across industries and domains. Whether you need clinical trial analysis for healthcare, experimental design for product optimization, time series forecasting for finance, or survey analysis for market research, we have done it before and know the specific considerations each domain requires.
We communicate results clearly. Statistical analysis is only valuable if stakeholders understand and act on the findings. We translate complex statistical concepts into clear recommendations, visualize uncertainty effectively, and provide the context needed for confident decision-making.
Requirements
What you need to get started
Research Question
requiredClear articulation of the question you want to answer or hypothesis you want to test.
Data Access
requiredAccess to relevant data with sufficient sample size for the analysis type required.
Context Documentation
requiredUnderstanding of how data was collected, known limitations, and business context.
Domain Expertise
recommendedAccess to subject matter experts who can validate findings and provide domain context.
Historical Benchmarks
optionalPrevious analysis results or industry benchmarks for comparison and validation.
Common Challenges We Solve
Problems we help you avoid
Sample Size Limitations
Multiple Testing Problems
Assumption Violations
Correlation vs Causation
Your Dedicated Team
Who you'll be working with
Lead Statistician
Designs methodology, validates assumptions, interprets results, ensures scientific rigor.
PhD in Statistics or 10+ years appliedResearch Analyst
Executes analysis, runs tests, creates visualizations and reports.
MS in Statistics or related fieldExperimentation Specialist
Designs A/B tests, implements tracking, analyzes experimental results.
5+ years in experimentation platformsData Analyst
Prepares data, validates quality, supports analysis workflow.
3+ years in data analysisHow We Work Together
Projects typically span 4-10 weeks depending on complexity, with iterative analysis and stakeholder review cycles.
Technology Stack
Modern tools and frameworks we use
R
Statistical computing
Python
Data analysis and modeling
SPSS
Enterprise statistics
SAS
Advanced analytics
Jupyter
Reproducible analysis
Stan/PyMC
Bayesian modeling
Value of Statistical Analysis
Rigorous analysis prevents costly mistakes and identifies high-impact opportunities.
Why We're Different
How we compare to alternatives
| Aspect | Our Approach | Typical Alternative | Your Advantage |
|---|---|---|---|
| Methodology | Proper statistical methods with assumption validation | Ad-hoc analysis without rigor | Valid, reproducible conclusions |
| Experimentation | Power analysis, sequential testing, proper controls | Simple A/B with arbitrary stopping | 5x fewer false positives |
| Interpretation | Effect sizes, confidence intervals, practical significance | P-values only | Actionable business insights |
| Documentation | Full methodology, reproducible code | Results-only reports | Auditable, defensible analysis |
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Let's discuss how we can help transform your business with statistical analysis & research services.