Fisher's Exact Test
Compute the two-sided Fisher exact p-value for a 2×2 contingency table with fixed margins. Tables above 10,000 total observations are rejected to bound exact enumeration.
Description
Compute the two-sided Fisher exact p-value for a 2×2 contingency table with fixed margins. Tables above 10,000 total observations are rejected to bound exact enumeration.
Fisher's Exact Test is a focused tool for the following task. Compute the two-sided Fisher exact p-value for a 2×2 contingency table with fixed margins. Tables above 10,000 total observations are rejected to bound exact enumeration. It reports Two-sided p-value from the values you provide rather than inventing measurements, coefficients, or professional judgment that are not part of the input.
When to use Fisher's Exact Test
Use this calculation to reproduce a defined quantitative method when the observations, units, sampling process, and assumptions match the method shown here.
- Top-left count
- Required integer.
- Top-right count
- Required integer.
- Bottom-left count
- Required integer.
- Bottom-right count
- Required integer.
The cited overview of Statistics supplies background for the terminology and domain context used by this tool.1
How Fisher's Exact Test works
Compute the two-sided Fisher exact p-value for a 2×2 contingency table with fixed margins. Tables above 10,000 total observations are rejected to bound exact enumeration. Inputs are interpreted exactly in the displayed units and the calculation returns the following fields without presentation rounding.
- Two-sided p-value
- Returned number.
Limitations and assumptions
- A numerical result does not by itself establish data quality, causation, representativeness, independence, distributional fit, or practical significance.
- Top-left count must be at least 0.
- Top-right count must be at least 0.
- Bottom-left count must be at least 0.
- Bottom-right count must be at least 0.
- Use finite inputs in the displayed units, preserve source measurements and assumptions, and independently verify consequential decisions.
Alternative or Complementary approaches
Inspect the underlying data, visualize its distribution, report uncertainty and sample size, and compare the result with a robust or domain-specific method where appropriate.
References
-
Statistics — Wikipedia contributors
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