There are alternatives to the p-value of 0.05 that some researchers advocate for. One approach is to use a more stringent threshold, such as 0.01 or 0.001, to reduce the likelihood of false positives. Another method is the use of Bayesian statistics, which incorporate prior knowledge or beliefs into the analysis. Additionally, emphasizing effect sizes and confidence intervals rather than p-values can provide a more nuanced understanding of the results.
