The p-value is defined as the probability that the difference between the means of two studies is not just due to chance. The values range between zero to one.

By convention, p-value <0.05 is considered statistically significant.

The p-value is used in deciding if the null hypothesis made before the start of the study should be accepted or rejected (i.e. it is an indication of the likelihood of making a type I error1. The p-value gives no information regarding type II error.

Practical points

  • if a statistic is appropriately below the p-value cutoff, this does not mean that the results of the study are valid or that a study is meaningful; it only tells you that the difference between the mean values of the test group and control group would occur by chance only <5% of the time
  • p=0.001 is not necessarily a 50x better study than p=0.05, the difference between the means must be taken into account
    • e.g. a study that shows 1% increased enhancement with a new MRI imaging agent at p=0.001 is not nearly as interesting as a new imaging agent that shows 20% increased enhancement at p=0.05
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    Figure 1: p value cutoff
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