Lab statistics, step by step
t-tests, ANOVA, chi-square, correlation and power analysis — paste your data, see the test and the result together.
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Biostatistics Calculator
20+ statistical tests in one tool. Paste your data, select a test, get instant results with plain-English interpretation. No login, no install.
Descriptive Statistics
Mean, SD, SEM, median, confidence intervals, and more. Paste one column of numbers.
t-Tests
Compare means between groups. Choose one-sample, two-sample, or paired.
One-Way ANOVA
Compare means across 2+ groups. Enter one group per line.
Non-Parametric Tests
Distribution-free alternatives to t-tests and ANOVA.
Correlation
Measure the strength and direction of the relationship between two variables.
Simple Linear Regression
Fit a line y = a + bx and test if the slope is significantly different from zero.
Power & Sample Size
Determine how many subjects you need, or assess the power of a completed study.
Effect Size
Quantify the magnitude of differences or relationships.
Diagnostic Test Evaluation
Calculate sensitivity, specificity, PPV, NPV, and more from a 2×2 table.
Multiple Comparison Corrections
Adjust p-values for multiple testing. Enter one p-value per line.
EC50 / IC50 Calculator
Fit a 4-parameter logistic curve to dose-response data.
Why Use an Online Biostatistics Calculator?
Statistical analysis is the backbone of evidence-based research. Whether you're comparing treatment groups in a clinical trial, analyzing gene expression levels, or validating diagnostic test accuracy, the right statistical test transforms raw numbers into actionable conclusions. Our biostatistics calculator runs entirely in your browser — no data leaves your device, making it suitable for sensitive patient data under DPDP/GDPR guidelines.
When to Use a t-Test vs. ANOVA
A t-test compares means between exactly two groups. Use it when you have a treatment group and a control group, or when comparing pre-and-post measurements. An ANOVA (Analysis of Variance) extends this to three or more groups — for example, comparing drug efficacy across multiple dosages. Running multiple t-tests instead of ANOVA inflates your false positive rate; ANOVA controls this with a single omnibus test, followed by post-hoc pairwise comparisons.
Effect Size: The Missing Metric
A p-value tells you whether an effect exists; effect size tells you how large it is. Cohen's d of 0.2 is small (barely detectable), 0.5 is medium (visible difference), and 0.8 is large (obvious clinical impact). A study with 10,000 participants can produce a statistically significant p-value for a trivially small effect. Always report effect sizes alongside p-values to give reviewers the full picture.
Choosing the Right Statistical Test
Selecting the appropriate test depends on your data type, distribution, and research question. Use this guide to pick the right tool.
| Research Question | Data Type | Parametric Test | Non-Parametric Alternative |
|---|---|---|---|
| Compare means of 2 groups | Continuous, normal | Independent t-test | Mann-Whitney U |
| Compare means of paired data | Continuous, normal | Paired t-test | Wilcoxon Signed-Rank |
| Compare means of 3+ groups | Continuous, normal | One-way ANOVA | Kruskal-Wallis |
| Association between 2 variables | Continuous | Pearson r | Spearman ρ |
| Predict Y from X | Continuous | Linear regression | Spearman regression |
| Compare proportions | Categorical | — | Chi-square / Fisher's exact |
| Evaluate a diagnostic test | Binary (TP/FP/FN/TN) | — | Sensitivity/Specificity |
Assumptions of Parametric Tests
- Normality — Data should be approximately normally distributed (check with Shapiro-Wilk test or Q-Q plot)
- Homogeneity of variance — Groups should have similar variances (check with Levene's test)
- Independence — Observations should be independent of each other
- Scale — Dependent variable should be continuous (interval or ratio)
Effect Size Conventions (Cohen's d)
| Effect Size | Cohen's d | Interpretation | Example |
|---|---|---|---|
| Small | 0.2 | Hard to see with naked eye | 5 kg weight loss in diet study |
| Medium | 0.5 | Moderate, visible difference | 10 kg weight loss in drug trial |
| Large | 0.8 | Obvious, clinically meaningful | Surgery vs medication outcome |
Common Mistakes in Biostatistics
- Using p-values without effect sizes — a significant p-value with n=10,000 can still mean a tiny, clinically irrelevant effect
- Ignoring assumptions — running a t-test on highly skewed data or ordinal scales
- Post-hoc power analysis — it adds no information beyond the p-value itself
- Multiple testing without correction — running 20 tests at α=0.05 guarantees at least 1 false positive
- Confusing statistical significance with practical significance — always report effect sizes and confidence intervals