Data Science & Operations
Statistical analysis, operations research, and data processing tools for professionals.
Our data science and operations calculators help analysts and researchers perform complex statistical analysis and operations research. From correlation analysis to Monte Carlo simulations, each tool provides detailed insights. You might also find our Perform regression analysis, Model normal distributions, Calculate business metrics, Forecast investment scenarios, Run hypothesis tests, Analyze student performance, Model biological data and Apply Bayesian methods helpful for related calculations.
Data Science & Operations Guide
Last updated: February 16, 2026
Last updated: February 16, 2026
What you can do in Data Science & Operations
- •Calculate correlation coefficients (Pearson, Spearman) with significance testing
- •Determine sample sizes for hypothesis tests with specified power and effect size
- •Run Monte Carlo simulations for risk analysis and probabilistic modeling
- •Compute ROI, NPV, and IRR for investment and project decisions
- •Analyze classification models with confusion matrices and precision/recall metrics
- •Apply queueing theory for capacity planning and wait time optimization
Accuracy, assumptions, and sources
- •Statistical tests assume independent, random samples unless otherwise specified in the tool.
- •Correlation calculates linear association. Non-linear relationships may show low r but high dependence.
- •Sample size calculations assume two-sided tests at α=0.05 unless you specify otherwise.
- •Monte Carlo simulations use pseudo-random numbers. More iterations improve estimate stability.
- •NPV/IRR calculations assume discrete cash flows at period end and constant discount rates.
- •Queueing models assume Markovian arrivals and service times (M/M/1, M/M/c).
Pick the right calculator fast
- If you need to measure variable relationships→Correlation Calculator
- If you're planning an experiment or survey→Sample Size Calculator
- If you want to simulate probabilistic outcomes→Monte Carlo Simulator
- If you need ROI, NPV, or IRR analysis→ROI / NPV / IRR Calculator
- If you're evaluating classification model performance→Confusion Matrix Calculator
- If you need queue wait times or capacity planning→Queueing Theory Calculator
- If you're solving optimization problems→Linear Programming Solver
- If you need quality metrics and sigma levels→Six Sigma Calculator
Common mistakes to avoid
- •Confusing correlation with causation. High r-values indicate association, not cause-effect.
- •Using underpowered sample sizes that miss real effects (Type II error).
- •Ignoring confidence intervals and only reporting point estimates—variability matters.
- •Running too few Monte Carlo iterations and treating results as precise.
- •Comparing NPV across projects with different lifespans without annualizing.
- •Applying parametric tests to non-normal data without checking assumptions.
- •Over-interpreting precision/recall without considering class imbalance in the dataset.
- •Using single discount rates for NPV when risk profiles differ across projects.
Editorial policy
- ✓All calculators provide educational estimates, not professional data science consulting.
- ✓Statistical methods follow standard textbooks and are documented in each tool.
- ✓We don't store your data. All calculations run client-side in your browser.
- ✓Results show confidence intervals and significance levels for proper interpretation.
- ✓Found an error? Email us at hello@everydaybudd.com and we'll fix it promptly.
- ✓Tools are updated when statistical best practices or analytical methods improve.
Top Picks
All Data Science & Operations Tools
Frequently Asked Questions
How do I choose the right sample size for my study?
Use our Sample Size Calculator by specifying effect size, power (typically 0.80), and significance level (typically 0.05). Larger effects need smaller samples. We show calculations for different test types: t-tests, proportions, correlations.
What's the difference between correlation and causation in these tools?
Our Correlation Calculator measures statistical association, not causation. A high r-value means variables move together, not that one causes the other. Establishing causation requires experimental design, not just statistical analysis.
How reliable are Monte Carlo simulation results?
Accuracy improves with more iterations. Our simulator defaults to enough runs for stable estimates. Results show confidence intervals so you can assess reliability. For critical decisions, run multiple simulations and compare.
Can these tools handle real business datasets?
Our calculators work with summary statistics (means, counts, standard deviations) rather than raw datasets. For large-scale data analysis, use Python/R with our calculators for verification and learning the underlying statistics.
How do I interpret confidence intervals correctly?
A 95% CI means: if we repeated the study many times, 95% of calculated intervals would contain the true population parameter. It does NOT mean 95% probability the true value is in this specific interval. Our calculators explain this distinction.
When should I use parametric vs. non-parametric tests?
Parametric tests (t-test, ANOVA) assume normal distributions and are more powerful when assumptions hold. Non-parametric tests (Mann-Whitney, Kruskal-Wallis) work with any distribution but have less power. Check normality before choosing.