What does a p-value of less than 0.05 indicate in a research study, as read by an Adult-Gerontology Acute Care Nurse Practitioner (AGACNP)?

Understanding P-Values in Research: Interpretation of P < 0.05

A p-value less than 0.05 indicates a significant result that would have happened only 5/100 times through chance alone. 1

What P-Values Mean

P-values represent the probability of obtaining a result at least as extreme as what was observed when the null hypothesis is true. 1 This statistical measure helps researchers determine if their findings are likely due to chance or represent a true effect.

When interpreting p-values:

• A p-value < 0.05 means there is less than a 5% probability (5 out of 100) that the observed difference occurred by chance alone 1
• Small p-values provide evidence against the null hypothesis (of no difference or no effect) 1
• The smaller the p-value, the stronger the evidence against the null hypothesis 1

Significance Thresholds and Interpretation

The conventional threshold for statistical significance is p < 0.05, though this is somewhat arbitrary:

• P-values between 0.05 and 0.01 represent modest evidence against the null hypothesis 1
• P-values < 0.001 represent strong evidence against the null hypothesis 1
• When p < 0.05, we reject the null hypothesis and consider the result "statistically significant" 2

Common Pitfalls in P-Value Interpretation

It's important to avoid these common misunderstandings:

• A p-value does not measure the size or clinical importance of an effect 3
• Statistical significance (p < 0.05) does not automatically mean clinical significance 3
• P-values should not be used as a crude decision-making tool that categorizes results as simply "positive" or "negative" 1
• P-values should be reported precisely (e.g., p = 0.03) rather than just stating p < 0.05 1

Beyond P-Values: Complete Statistical Reporting

For comprehensive interpretation of research findings:

• Effect sizes should be reported alongside p-values to understand the magnitude of differences 3
• Confidence intervals provide information about the precision of the estimated effect 3
• Point estimates (mean differences, odds ratios, hazard ratios) give the actual measured effect 1

Application to Clinical Decision-Making

When evaluating research for clinical application:

• Consider both statistical significance (p < 0.05) and clinical significance (meaningful impact on patient outcomes) 2
• Evaluate the quality and design of the study, not just the p-value 1
• Remember that a single study with p < 0.05 doesn't provide definitive proof 2

Conclusion for AGACNP Interpretation

When an AGACNP reads a research report with p < 0.05, they should conclude that:

• The result is statistically significant 1
• The finding would occur by chance alone only 5 out of 100 times (or less) 1, 2
• The evidence suggests rejecting the null hypothesis of no difference 1
• Further evaluation of effect size and clinical significance is still necessary 3

Therefore, option D is correct: "A significant result that would have happened only 5/100 times through chance."