Data Science

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.

150+
Research Projects
500+
A/B Tests Run
95%+ confidence
Statistical Significance
50+
Publications Supported

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

95%+ statistical
Confidence Level
Rigorous significance testing
80%+ power
Power Analysis
Properly powered studies
4-10 weeks
Time to Results
Methodology to insights
100%
Reproducibility
Fully documented analysis

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

required

Clear articulation of the question you want to answer or hypothesis you want to test.

Data Access

required

Access to relevant data with sufficient sample size for the analysis type required.

Context Documentation

required

Understanding of how data was collected, known limitations, and business context.

Domain Expertise

recommended

Access to subject matter experts who can validate findings and provide domain context.

Historical Benchmarks

optional

Previous analysis results or industry benchmarks for comparison and validation.

Common Challenges We Solve

Problems we help you avoid

Sample Size Limitations

Impact: 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

Impact: 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

Impact: 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

Impact: 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

Who you'll be working with

Lead Statistician

Designs methodology, validates assumptions, interprets results, ensures scientific rigor.

PhD in Statistics or 10+ years applied

Research Analyst

Executes analysis, runs tests, creates visualizations and reports.

MS in Statistics or related field

Experimentation Specialist

Designs A/B tests, implements tracking, analyzes experimental results.

5+ years in experimentation platforms

Data Analyst

Prepares data, validates quality, supports analysis workflow.

3+ years in data analysis

How 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.

95%+ certainty
Decision Confidence
Per analysis
15-30% lift
A/B Test Wins
Successful tests
5x reduction
Avoided False Positives
Vs. ad-hoc testing
Publication-ready
Research Validity
Peer-reviewed standards

Why We're Different

How we compare to alternatives

AspectOur ApproachTypical AlternativeYour Advantage
MethodologyProper statistical methods with assumption validationAd-hoc analysis without rigorValid, reproducible conclusions
ExperimentationPower analysis, sequential testing, proper controlsSimple A/B with arbitrary stopping5x fewer false positives
InterpretationEffect sizes, confidence intervals, practical significanceP-values onlyActionable business insights
DocumentationFull methodology, reproducible codeResults-only reportsAuditable, defensible analysis

Ready to Get Started?

Let's discuss how we can help transform your business with statistical analysis & research services.