Fisher's Exact Test Calculator

Real-time statistical analysis for 2x2 contingency tables

Statistical Tool Research Real-Time

Contingency Table Input

Enter your data into the 2x2 contingency table below. Values must be non-negative integers.

Condition A Condition B Total
Group 1 15
Group 2 15
Total 13 17 30
Table Summary

Grand Total: 30

Row 1 Total: 15

Row 2 Total: 15

Column 1 Total: 13

Column 2 Total: 17

Expected Values: Calculated

Test Results

P-value
0.0315
Statistically Significant
Odds Ratio
8.000
Strong Association
Interpretation

The Fisher's Exact Test shows a statistically significant association between the groups and conditions (p = 0.0315). The odds ratio of 8.000 indicates that Group 1 is 8 times more likely to have Condition A than Condition B compared to Group 2.

Additional Statistics
Test Type: Two-tailed
Alpha Level: 0.05
Confidence: 95%
Sample Size: 30
Expected Min Value: 6.5
Chi-square Approx: 4.81

Data Visualization

Condition Distribution by Group

Visualization will appear here

(In a full implementation, charts would be generated)
Expected vs Observed Values
Cell Observed Expected Difference
Group1-ConditionA 10 6.5 +3.5
Group1-ConditionB 5 8.5 -3.5
Group2-ConditionA 3 6.5 -3.5
Group2-ConditionB 12 8.5 +3.5

Understanding Fisher's Exact Test: A Practical Guide

What is Fisher's Exact Test?

Fisher's Exact Test is a statistical significance test used to analyze contingency tables. It's particularly useful when you have small sample sizes, as it provides an exact p-value rather than relying on approximations like the Chi-square test.

When to Use This Test
  • Small sample sizes (typically when any expected cell count is less than 5)
  • 2x2 contingency tables with two categorical variables
  • Medical research comparing treatment outcomes
  • Biological studies examining presence/absence of traits
  • Market research comparing preferences between groups
How to Use This Calculator
  1. Enter your data into the 2x2 contingency table (non-negative integers only)
  2. Select your test type: two-tailed, left-tailed, or right-tailed
  3. Choose your significance level (alpha) - typically 0.05
  4. View your results instantly: p-value, odds ratio, and interpretation
  5. Use the export buttons to save your results for reporting
Interpreting the Results

The key output is the p-value:

  • p ≤ 0.05: Statistically significant - reject the null hypothesis
  • p > 0.05: Not statistically significant - insufficient evidence to reject null hypothesis

The odds ratio indicates the strength of association between variables. An odds ratio of 1 means no association, while values further from 1 indicate stronger associations.

Key Statistical Terms
P-value
Probability of observing results as extreme as yours if the null hypothesis is true
Odds Ratio
Measure of association between two binary variables
Alpha (α)
Significance threshold; probability of Type I error
Contingency Table
Table showing frequency distribution of variables
Two-tailed Test
Tests for any difference, regardless of direction
Common Applications
  • Clinical trial analysis
  • A/B testing in marketing
  • Genetic association studies
  • Quality control in manufacturing
  • Survey response analysis