Confronting p-hacking: addressing p-value dependence on sample size

Biomedical research has come to rely on p-values to determine potential translational impact. The p-value is routinely compared with a threshold commonly set to 0.05 to assess the significance of the null hypothesis. Whenever a large enough dataset is available, this threshold is easily reachable. This phenomenon is known as p-hacking and it leads to spurious conclusions. Herein, we propose a systematic and easy-to-follow protocol that models the p-value as an exponential function to test the existence of real statistical significance. This new approach provides a robust assessment of the null hypothesis with accurate values for the minimum data-size needed to reject it. An in-depth study of the model is carried out in both simulated and experimentally-obtained data. Simulations show that under controlled data, our assumptions are true. The results of our analysis in the experimental datasets reflect the large scope of this approach in common decision-making processes.