Quantitative Market Research Design & Analysis
Design and analyze rigorous market research with statistical confidence
What You Can Do
Claude helps you design statistically sound market research studies from hypothesis formation through analysis. You'll calculate sample sizes, statistical power, and interpret confidence intervals and hypothesis tests with professional rigor. Transform raw survey and experiment data into actionable business insights backed by solid statistics.
Features
Develop rigorous research frameworks aligned with your business questions, including hypothesis specification, study design type (survey, experiment, observational), and measurement protocols.
Calculate required sample sizes to achieve your target statistical power, accounting for effect size, significance level, and study design complexity.
Select and interpret appropriate statistical tests (t-tests, chi-square, ANOVA, regression) based on your data type and research question.
Quantify uncertainty in your results with confidence intervals, showing the range of plausible true values for your key metrics.
Determine adequate sample sizes before data collection, balancing statistical rigor with practical constraints like budget and timeline.
Check normality, homogeneity of variance, and other assumptions required for different statistical tests, with alternative methods when assumptions are violated.
Translate p-values, effect sizes, and confidence intervals into clear business insights distinguishing statistical significance from practical significance.
Analyze experiment results with appropriate statistical tests, accounting for multiple comparisons and sequential testing when applicable.
Example Output
Customer Satisfaction Survey Design
Sample Size Recommendation: 385 respondents (95% confidence, 5% margin of error, 50% expected satisfaction) Power Analysis: 80% power to detect 10-point difference on 100-point scale Recommended Test: Two-sample t-test for comparison groups
A/B Test Analysis Results
Control Conversion: 12.0% (n=2,000) Treatment Conversion: 15.0% (n=2,000) 95% Confidence Interval: [1.2%, 5.8% improvement] P-value: 0.0032 (statistically significant) Effect Size (Cohen's h): 0.069 (small but meaningful lift) Interpretation: Strong evidence of improvement; recommend rollout with monitoring
What's Included
- Research Design Framework: Step-by-step templates for defining research questions, hypotheses, variables, and study design appropriate to your situation.
- Power & Sample Size Calculator: Interactive guidance for determining required sample sizes based on effect size, significance level, and desired power.
- Statistical Test Selection Guide: Decision trees for choosing the right test (parametric vs. non-parametric, univariate vs. multivariate) based on your data.
- Confidence Interval Interpreter: Calculate and explain confidence intervals for proportions, means, and differences, with interpretation guidance for stakeholders.
- Assumption Checking Toolkit: Methods for verifying normality, homogeneity of variance, independence, and other assumptions, with robust alternatives when assumptions fail.
- Results Reporting Templates: Professional summary formats for presenting findings to executives, combining statistical rigor with business relevance.
Who It's For
- Market Research Analysts
- Product Managers
- Data Analysts & Scientists
- Business Strategists
- UX Researchers
Best For
- Designing customer surveys and experiments
- Calculating sample sizes for market research
- Analyzing A/B test and experiment results
- Interpreting survey data with statistical rigor
- Validating business hypotheses with confidence intervals
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