From the Guidelines
A P value represents the probability of observing results at least as extreme as those found in a study if the null hypothesis were true, and it should be interpreted in the context of effect sizes, confidence intervals, and clinical context. The P value is a measure of the strength of evidence against the null hypothesis, but it does not directly indicate the probability that the hypothesis is correct or the size of the effect. For example, a P value of 0.03 means there's a 3% chance of seeing the observed results (or more extreme) if no real effect exists 1. When interpreting P values, it's essential to consider them alongside other metrics, such as effect sizes and confidence intervals, to determine the clinical significance of the findings.
Key Points to Consider
- The P value does not measure the size or importance of an effect, but rather the strength of evidence against the null hypothesis 1.
- A P value less than 0.05 suggests that the observed results are unlikely to have occurred by chance alone, but it does not necessarily mean that the finding is clinically meaningful or practically significant 1.
- The interpretation of P values should be done in a continuous graded manner, with smaller P values indicating stronger evidence against the null hypothesis 1.
- The use of Bayes factors may be a valuable alternative for assessing the strength and precision of evidence 1.
- It's crucial to report P values precisely, rather than stating that they are less than a certain level of significance, and to consider the clinical context and effect sizes when making decisions 1.
From the Research
Definition of P Value
- The P value, or probability value, is a key concept in statistical hypothesis testing, representing the probability of observing results at least as extreme as those observed, assuming that the null hypothesis is true 2, 3, 4, 5.
- It is a measure of the strength of evidence against the null hypothesis, with smaller P values indicating stronger evidence against the null hypothesis.
- In general, a P value of less than 0.05 is considered statistically significant, meaning that the observed results are unlikely to occur by chance if the null hypothesis is true.
Interpretation of P Value
- The P value does not indicate the probability that the null hypothesis is true or false, but rather the probability of observing the results given that the null hypothesis is true 2, 3.
- A small P value does not necessarily mean that the alternative hypothesis is true, but rather that the observed results are unlikely to occur by chance if the null hypothesis is true.
- It is also important to consider the context and limitations of the study, as well as the potential for type I errors (false positives) or type II errors (false negatives) when interpreting P values 4, 5.
Examples of P Value in Research Studies
- In the study on mepolizumab for COPD, the P value for the primary endpoint was 0.01, indicating a statistically significant reduction in exacerbations compared to placebo 2.
- In the study on BCG revaccination for tuberculosis, the P value for the primary endpoint was not significant, indicating no difference in sustained QFT test conversion between the BCG vaccine and placebo groups 3.
- In the study on antihypertensive medication timing, the P value for the primary endpoint was 0.70, indicating no difference in cardiovascular risk between bedtime and morning administration of antihypertensive medications 4.