Student was a brewer
The “Student” in Student’s t-test was William Gosset, a Guinness brewer who published under a pen name to protect the brewery’s trade secrets.
Pub origin
BioStats Quick Calculator — t-test, Chi-square, One-way ANOVA
Data & Test Selection
Examples: “control vs treatment t-test”, “3 groups one-way ANOVA”, “2×3 contingency table chi-square”.
Results
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Detailed Results Table
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Run a calculation to see the summary, chart placeholder, and table here.
Understanding Common BioStats Tests
Choosing the right statistical test is crucial for drawing valid conclusions from your biological data. Here's a brief overview of the tests available in this calculator:
Student's t-test
The t-test is used to compare the means of two groups. It's ideal when you want to determine if there is a significant difference between the averages of two independent samples (e.g., control vs. treatment group).
- Assumptions: Data should be normally distributed, and variances should be roughly equal (though variations like Welch's t-test can handle unequal variances).
- Input: Two sets of numerical data.
- Output: t-statistic, degrees of freedom, and p-value.
Chi-square Test (χ²)
The chi-square test is used to determine if there is a significant association between two categorical variables. It compares observed frequencies with expected frequencies. It's commonly used in genetics (e.g., Mendelian ratios), ecology (e.g., species distribution), or epidemiology (e.g., disease prevalence across categories).
- Assumptions: Data must be frequencies or counts, not percentages or ratios. Expected frequencies should not be too small (typically, no more than 20% of expected counts are less than 5).
- Input: Observed frequencies organized in a contingency table (e.g., enter counts per category for each group).
- Output: Chi-square statistic, degrees of freedom, and p-value.
One-way ANOVA (Analysis of Variance)
ANOVA is used to compare the means of three or more independent groups. It determines if there is a statistically significant difference between the means of these groups. If ANOVA shows a significant difference, post-hoc tests (not included in this simple calculator) are typically used to identify which specific groups differ from each other.
- Assumptions: Data should be normally distributed within each group, and variances should be roughly equal across groups.
- Input: Three or more sets of numerical data.
- Output: F-statistic, degrees of freedom, and p-value.
P-value Interpretation: The p-value helps you determine the significance of your results. A commonly used threshold is 0.05. If your p-value is less than 0.05, it suggests that the observed differences are statistically significant, meaning they are unlikely to have occurred by random chance.
FAQ
Do my data leave my device?
No. All computations run entirely in your browser (client-side). Nothing is uploaded or stored on a server.
What assumptions do these tests require?
t-test and one-way ANOVA assume approximate normality and similar variances; chi-square assumes count data with adequately large expected counts. Check assumptions before drawing conclusions.
Does this include post-hoc tests or effect sizes?
This quick calculator provides core test statistics and p-values. Post-hoc comparisons, multiple-testing corrections, and effect sizes should be calculated separately.
Is this medical advice?
No. This tool is for education/research support only and does not replace professional statistical or medical guidance.
5 Fun Facts about Quick Biostats
Chi-square hates tiny boxes
Expected counts under ~5 per cell can inflate χ² p-values; collapsing sparse categories often stabilizes the test.
Assumption check
ANOVA = regression in disguise
One-way ANOVA and dummy-coded linear regression produce the same F-statistic and p-value—they’re two views of the same model.
Model twins
Shuffle to sanity-check
Randomly permuting group labels and re-running a test (a quick permutation test) shows how often “significance” appears by chance.
Null play
Huge N shrinks p
With very large samples, even tiny effect sizes can look “significant.” Pair p-values with effect sizes or confidence intervals.
Practical vs. statistical