Predictive power of statistical significance

A statistically significant research finding should not be defined as a P -value of 0.05 or less, because this definition does not take into account study power.Statistical significance was originally defined by Fisher RA as a P -value of 0.05 or less.According to Fisher, any finding that is likely to occur by random variation no more than 1 in 20 times is considered significant.Neyman J and Pearson ES subsequently argued that Fisher's definition was incomplete.They proposed that statistical significance could only be determined by analyzing the chance of incorrectly considering a study finding was significant (a Type Ⅰ error) or incorrectly considering a study finding was insignificant (a Type Ⅱ error).Their definition of statistical significance is also incomplete because the error rates are considered separately, not together.A better definition of statistical significance is the positive predictive value of a P -value, which is equal to the power divided by the sum of power and the P -value.This definition is more complete and relevant than Fisher's or Neyman-Peason's definitions, because it takes into account both concepts of statistical significance.Using this definition, a statistically significant finding requires a P -value of 0.05 or less when the power is at least 95%, and a P -value of 0.032 or less when the power is 60%.To achieve statistical significance, P -values must be adjusted downward as the study power decreases.