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.
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 & Prerequisites
Understand what you need to get started and what we can help with
Required(3)
Research Question
Clear articulation of the question you want to answer or hypothesis you want to test.
Data Access
Access to relevant data with sufficient sample size for the analysis type required.
Context Documentation
Understanding of how data was collected, known limitations, and business context.
Recommended(1)
Domain Expertise
Access to subject matter experts who can validate findings and provide domain context.
Optional(1)
Historical Benchmarks
Previous analysis results or industry benchmarks for comparison and validation.
Common Challenges & Solutions
Understand the obstacles you might face and how we address them
Sample Size Limitations
Insufficient data leads to inconclusive results or false conclusions.
Our Solution
Power analysis and sample size planning before analysis, with appropriate methods for small samples when needed.
Multiple Testing Problems
Running many tests inflates false positive rates, leading to spurious findings.
Our Solution
Proper experimental design, pre-registration of hypotheses, and correction methods (Bonferroni, FDR) control error rates.
Assumption Violations
Using methods when assumptions are violated produces unreliable results.
Our Solution
Rigorous assumption testing with robust alternatives (non-parametric methods, bootstrapping) when needed.
Correlation vs Causation
Confusing correlation with causation leads to ineffective interventions.
Our Solution
Causal inference methods, experimental design when possible, and clear communication of what conclusions are supported.
Your Dedicated Team
Meet the experts who will drive your project to success
Lead Statistician
Responsibility
Designs methodology, validates assumptions, interprets results, ensures scientific rigor.
Experience
PhD in Statistics or 10+ years applied
Research Analyst
Responsibility
Executes analysis, runs tests, creates visualizations and reports.
Experience
MS in Statistics or related field
Experimentation Specialist
Responsibility
Designs A/B tests, implements tracking, analyzes experimental results.
Experience
5+ years in experimentation platforms
Data Analyst
Responsibility
Prepares data, validates quality, supports analysis workflow.
Experience
3+ years in data analysis
Engagement Model
Projects typically span 4-10 weeks depending on complexity, with iterative analysis and stakeholder review cycles.
Success Metrics
Measurable outcomes you can expect from our engagement
Confidence Level
95%+ statistical
Rigorous significance testing
Typical Range
Power Analysis
80%+ power
Properly powered studies
Typical Range
Time to Results
4-10 weeks
Methodology to insights
Typical Range
Reproducibility
100%
Fully documented analysis
Typical Range
Value of Statistical Analysis
Rigorous analysis prevents costly mistakes and identifies high-impact opportunities.
Decision Confidence
95%+ certainty
Within Per analysis
A/B Test Wins
15-30% lift
Within Successful tests
Avoided False Positives
5x reduction
Within Vs. ad-hoc testing
Research Validity
Publication-ready
Within Peer-reviewed standards
“These are typical results based on our engagements. Actual outcomes depend on your specific context, market conditions, and organizational readiness.”
Why Choose Us?
See how our approach compares to traditional alternatives
| Aspect | Our Approach | Traditional Approach |
|---|---|---|
| Methodology | Proper statistical methods with assumption validation Valid, reproducible conclusions | Ad-hoc analysis without rigor |
| Experimentation | Power analysis, sequential testing, proper controls 5x fewer false positives | Simple A/B with arbitrary stopping |
| Interpretation | Effect sizes, confidence intervals, practical significance Actionable business insights | P-values only |
| Documentation | Full methodology, reproducible code Auditable, defensible analysis | Results-only reports |
Technologies We Use
Modern, battle-tested technologies for reliable and scalable solutions
R
Statistical computing
Python
Data analysis and modeling
SPSS
Enterprise statistics
SAS
Advanced analytics
Jupyter
Reproducible analysis
Stan/PyMC
Bayesian modeling
Ready to Get Started?
Let's discuss how we can help you with data science.