Fisher's Exact Test Calculator

Find the two-tailed p-value for a 2x2 table.

Two-tailed p-value 0.035

Formula: Sum of hypergeometric probabilities <= observed

Step-by-step with your numbers:
1. Values used:
2. a (row1, col1) = 8
3. b (row1, col2) = 2
4. c (row2, col1) = 1
5. d (row2, col2) = 5
6.
7. Two-tailed p-value = 0.035
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Fisher's exact test gives an exact p-value for small 2x2 contingency tables.

How the Math Works

Fisher's Exact Test calculates the probability of observing a given 2x2 contingency table or something more extreme, assuming the null hypothesis is true. It uses the hypergeometric distribution to compute the exact probability of each possible table configuration with the same marginal totals. The two-tailed p-value is obtained by summing the hypergeometric probabilities of all tables that have probabilities less than or equal to the observed table's probability, providing an exact test without relying on approximations.

Practical Applications

To apply this calculator, enter the four values from your 2x2 contingency table into the designated input fields. For example, if studying the relationship between a treatment and recovery outcome, you would input the counts of treated-recovered, treated-not recovered, control-recovered, and control-not recovered. The calculator then computes the two-tailed p-value to determine if there is a statistically significant association between your two categorical variables.

Day-to-Day Use

This test helps you make data-driven decisions in everyday situations involving comparisons between two groups. Whether evaluating if a new fertilizer actually improves plant growth, determining if a marketing campaign effectively increased customer engagement, or assessing whether a medical treatment provides real benefits, Fisher's Exact Test gives you the confidence to distinguish genuine effects from random chance in your observations.

Worked example

Table 8/2/1/5 gives a two-tailed p of about 0.035.

FAQ

Why not chi-square?

Fisher's test is exact and preferred when expected counts are small.