# Saying ‘no’ with confidence: statistical approaches to test for the absence of an effect

## TL;DR

This paper explains how biologists can confidently test for the absence of an effect using statistical methods beyond traditional p-values.

## Contribution

The paper introduces accessible statistical approaches like equivalence tests and Bayes factors to test for the absence of an effect.

## Key findings

- Equivalence tests, confidence intervals, and credible intervals can be used to investigate the absence of a meaningful effect.
- Likelihood ratios and Bayes factors are useful for testing the absence of any effect.
- Accessible software supports these statistical approaches for biologists.

## Abstract

Publishing non-significant findings is essential for the progress of science. However, many of us forget that ‘absence of evidence is not evidence of absence’ and believe that a statistically non-significant result is evidence of no effect. Regrettably, and despite the null hypothesis being simple, elegant and often underpinned by evidenced or reasoned convictions, conventional p-value analysis can only argue against the null hypothesis, never in favour of it. Here, I provide a quick-and-easy guide to simple yet powerful statistical options available to biologists for investigating the absence of a meaningful effect, namely equivalence tests, confidence intervals and credible intervals; or the absence of any effect, namely likelihood ratios and Bayes factors. These approaches, supported by accessible software, allow biologists to draw direct conclusions about the null hypothesis.