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.
20+
Statistical Tests
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Instant Results
Upload your data — CSV, TSV, or Excel (.xlsx)
Drag & drop or click to browse. Columns auto-detected for mapping.
Data Preview
Descriptive Statistics
Mean, SD, SEM, median, confidence intervals, and more. Paste one column of numbers.
Supports spaces, tabs, commas, or one value per line.
Load example: cell viability (%)
t-Tests
Compare means between groups. Choose one-sample, two-sample, or paired.
Load example: treatment vs control
Uncheck for Welch's t-test (does not assume equal variances).
One-Way ANOVA
Compare means across 2+ groups. Enter one group per line.
Load example: 3 treatment groups
Non-Parametric Tests
Distribution-free alternatives to t-tests and ANOVA.
Load example: non-normal data
Correlation
Measure the strength and direction of the relationship between two variables.
Load example: dose vs response
Simple Linear Regression
Fit a line y = a + bx and test if the slope is significantly different from zero.
Load example: dose-response
Power & Sample Size
Determine how many subjects you need, or assess the power of a completed study.
0.2 = small, 0.5 = medium, 0.8 = large
Typically 0.80 (80%)
Effect Size
Quantify the magnitude of differences or relationships.
Load example
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.
Load example: 7 tests
EC50 / IC50 Calculator
Fit a 4-parameter logistic curve to dose-response data.
Load example: drug dose-response
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
Frequently Asked Questions
What statistical tests does this calculator support?
The calculator supports 20+ tests organized into categories: Descriptive Statistics (mean, SD, SEM, CI, five-number summary), Parametric Tests (one-sample t-test, two-sample t-test, paired t-test, one-way ANOVA), Non-Parametric Tests (Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, chi-square, Fisher's exact), Correlation & Regression (Pearson, Spearman, linear regression), Sample Size & Power Analysis, Effect Size (Cohen's d, Hedges' g, eta-squared, odds ratio, relative risk), Diagnostic Test Evaluation (sensitivity, specificity, PPV, NPV), Multiple Comparison Corrections (Bonferroni, Holm), and EC50/IC50 dose-response modeling.
How do I enter data for group comparisons?
For two-group tests, paste one group per line in the Group A and Group B fields. Values within each group can be separated by spaces, tabs, or commas. For one-way ANOVA, enter one group per line in the data field. Each line becomes one group. The calculator handles any sample size.
What does the p-value mean?
The p-value is the probability of observing your result (or more extreme) if the null hypothesis is true. A p-value less than 0.05 is typically considered statistically significant — meaning there is less than a 5% chance the result occurred by random chance alone. However, statistical significance does not always imply practical significance. Always consider effect size alongside p-values.
When should I use a non-parametric test?
Use non-parametric tests when: (1) your data is not normally distributed, (2) you have ordinal or ranked data, (3) your sample size is very small (n < 10), or (4) you have significant outliers. These tests make fewer assumptions about the data distribution but are generally less powerful than parametric tests when the assumptions are met.
What is effect size and why does it matter?
Effect size measures the magnitude of a difference or relationship, independent of sample size. Cohen's d of 0.2 is small, 0.5 is medium, and 0.8 is large. Effect size matters because a statistically significant p-value with a very large sample size can still represent a trivially small effect. Always report effect sizes alongside p-values for a complete picture.
How do I calculate sample size for my study?
Go to the Power & Sample Size tab, select your test type, and enter your expected effect size (Cohen's d), desired power (typically 0.80), and significance level (typically 0.05). The calculator returns the minimum sample size per group. Always add 10-20% extra to account for dropouts.
What is the difference between Pearson and Spearman correlation?
Pearson correlation measures the linear relationship between two continuous variables and assumes both are normally distributed. Spearman correlation measures the monotonic (rank-order) relationship and does not assume normality. Use Pearson when your data meets parametric assumptions; use Spearman when it doesn't or when you have ordinal data.