2025/10/01 by Lewis G. Halsey · 4 citations
Decision Sciences · Mathematics · #Alternative hypothesis #Bayes factor #Bayes' theorem #Equivalence (formal languages) #Meta-analysis and systematic reviews #Null (SQL) #Null hypothesis #Reliability and Agreement in Measurement #Statistical Methods in Clinical Trials #Statistical evidence #Statistical hypothesis testing
paper · doi:10.1098/rsbl.2025.0506
published in Biology Letters 21(10), 20250506 (Royal Society)
openalex publication_date 2025/10/01 · openalex created_date 2025/10/29 · openalex updated_date 2026/08/05
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.