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September 10, 2025Open Access

Absence of evidence is not evidence of absence: Testing for equivalence

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Absence of evidence does not confirm the absence of an effect, highlighting the limitations of p-values.
Investigating equivalence tests such as TOST helps clarify interpretations of p-values in statistical analysis.
Using R package TOSTER, researchers can effectively perform TOST equivalence tests for clearer evidence evaluation.
This approach underscores the significance of proper interpretation in statistical results and scientific communication.

Implication

Equivalence testing applies TOST to analyze evidence from data, indicating no true effect exists.

Abstract

See the follow up post where I introduce my R package and spreadsheet TOSTER to perform TOST equivalence tests, and link to a practical primer on this topic. When you find p > 0.05, you did not observe surprising data, assuming there is no true effect.

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Cite This Study

Daniel Lakens (2016) studied this question.

synapsesocial.com/papers/68c1e17054b1d3bfb60fea32 https://doi.org/10.59350/cxs3z-mkf07

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Implication

Equivalence testing applies TOST to analyze evidence from data, indicating no true effect exists.

Absence of evidence is not evidence of absence: Testing for equivalence

Key Points

Implication

Abstract

Discussion

Cite This Study

Absence of evidence is not evidence of absence: Testing for equivalence

Key Points

Implication

Abstract

Discussion

Cite This Study

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