hypothesize: A Consistent Interface for Statistical Tests

R’s hypothesis testing functions are inconsistent. t.test() returns a different structure than chisq.test(). Writing generic code that works across tests is painful. hypothesize fixes this with a unified API where every test returns the same interface.

The Problem

Different R tests return incompatible objects:

t.test(x, y)$p.value        # Works
chisq.test(x, y)$p.value    # Also works
my_custom_test(x, y)$???    # Who knows?
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You cannot write generic code that works across tests without knowing the internals of each one.

The Fix

hypothesize defines a consistent interface:

test <- lrt(model_null, model_alt)  # Likelihood ratio test
pval(test)          # Extract p-value
test_stat(test)     # Extract test statistic
dof(test)           # Extract degrees of freedom
is_significant_at(test, 0.05)  # Boolean check
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All tests, built-in or custom, implement the same generic functions. The interface is the same whether you are doing a likelihood ratio test, a Wald test, or a Z-test.

Integration with likelihood.model

The package works with likelihood.model, so likelihood ratio tests on any model are straightforward:

lrt(null_model, alternative_model)  # Automatic LRT
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You specify the models. The package computes the test statistic, degrees of freedom, and p-value. Same interface as every other test.

The Point

Tests are objects you manipulate, not functions with incompatible return types. You can write test-agnostic pipelines. You can wrap your own custom tests in the same interface. This is generic programming applied to hypothesis testing: a consistent abstraction over heterogeneous implementations.

R package – Works with likelihood.model – Documentation – GitHub