## A better alternative to using *α* = 0.05 for null hypothesis significance tests in biological research

For several decades, null hypothesis significance testing has been under attack 1–3. It nevertheless remains widely used in biological research despite suggestions that Bayesian 4, confidence interval 5, or AIC 6 approaches are more valid and/or appropriate. One explanation for the continued use of null hypothesis significance testing in biological research is that biologists are ignorant of alternative approaches and/or resistant to change. Another explanation is that null hypothesis significance tests have real utility for biologists as a statistical decision-making tool 7–9. While the former may occasionally be the truth, we think the latter more commonly explains the use of null hypothesis testing. Many of the drawbacks attributed to null hypothesis significance testing revolve around the use of an arbitrary significance threshold (i.e. *α* = 0.05). A better alternative has recently been described 10 that allows the user to find the best possible compromise between Type I and Type II error rates for their particular study design. Application of the optimal *α* approach has the potential to improve results interpretation and reduce overall error rates in biological research.

Problems arising from using the *α* = 0.05 significance level can be easily illustrated by comparing the results of published null hypothesis significance tests using the optimal *α* approach with those obtained using *α* = 0.05. Mudge et al. 11 has shown that for null hypothesis significance tests conducted under the Canadian Environmental Effects Monitoring program, 12% of tests would have reached different conclusions had they selected an optimal *α* that minimized the a priori probability of making an error (i.e. Type I or II). There are clear consequences of wrong conclusions associated with choosing an inappropriate statistical decision-making threshold for applied environmental monitoring research. There can also be important consequences associated with wrong conclusions associated with choosing an inappropriate *α* level for pure, theoretical biological research. Here, we use three recently published papers 12–14 examining the pace-of-life syndrome (POLS) hypothesis – which predicts specific linkages among life-history, physiological, and behavioral characteristics at among-species, among-population, and within-population levels of biological organization – to compare how conclusions would change using optimal *α* levels versus the traditional *α* = 0.05 statistical threshold.