Today, the probability of successfully publishing negative/null results in biomedical research is lower than that for positive results. For instance, negative results repeatedly remain in lab books or drawers, or are not being published because they are rejected by scientific journals. These seemingly positive results then lead to further studies which build on the supposedly proven effect. In contrast, if all studies, irrespectively of their results, were to be published after complying with good scientific practice, a false result could be disproven more quickly.
The mathematical model presented in this paper by Steinfath et al. provides evidence that higher-powered experiments can save resources in the overall research process without generating excess false positives: a sufficiently high number of test animals for a single experiment increases the likelihood of achieving correct and reproducible results at the first attempt. In the long run, unnecessary follow-up tests with animals based on false assumptions can be avoided this way. Hence, the use of more test animals in a single experiment can reduce the total number of animals used – and speed up the development of new therapies.