To P or Not to P: Backing Bayesian Statistics

Otolaryngol Head Neck Surg. 2017 Dec;157(6):915-918. doi: 10.1177/0194599817739260.


In biomedical research, it is imperative to differentiate chance variation from truth before we generalize what we see in a sample of subjects to the wider population. For decades, we have relied on null hypothesis significance testing, where we calculate P values for our data to decide whether to reject a null hypothesis. This methodology is subject to substantial misinterpretation and errant conclusions. Instead of working backward by calculating the probability of our data if the null hypothesis were true, Bayesian statistics allow us instead to work forward, calculating the probability of our hypothesis given the available data. This methodology gives us a mathematical means of incorporating our "prior probabilities" from previous study data (if any) to produce new "posterior probabilities." Bayesian statistics tell us how confidently we should believe what we believe. It is time to embrace and encourage their use in our otolaryngology research.

Keywords: Bayesian; NHST; P value; frequentist; inference; numeracy; posterior; prior; probability; statistics.

Publication types

  • Review

MeSH terms

  • Bayes Theorem*
  • Biomedical Research / statistics & numerical data*
  • Data Interpretation, Statistical
  • Humans
  • Otolaryngology / statistics & numerical data*
  • Probability
  • Research Design