The traditional definition of statistical significance (p <= 0.05) doesn't consider the likelihood that a test result is correct. A new approach suggests that a test is statistically significant if it has a 95% chance of being right. This means the p-value should be less than the study's power divided by 19. This new method takes into account both the test's accuracy and its power, giving a more reliable measure of significance.