Considering that a researcher can test at the <p = .05 (95%), p = .01 (99%), or p = .001 (99.9%) level for statistical significance, how would you use these three levels in relationship to risk to patients in implementing the decisions of your research?
They are your patients!
When it comes to implementing research decisions that have potential implications for patient risk, understanding the concept of statistical significance and using appropriate levels becomes crucial. The three commonly used levels, p = .05, p = .01, and p = .001, represent the thresholds at which researchers consider a result to be statistically significant. Here's how you can use these levels in relation to patient risk:
1. p = .05 (95% level): This level is commonly used in many research studies. It means that if the p-value resulting from your analysis is less than .05, you can conclude that the observed effect is statistically significant at a 95% confidence level. In the context of patient risk, this suggests that there is a 5% chance (or 1 in 20) that the observed effect is due to chance rather than a true effect. Therefore, you would implement decisions based on this level with caution, knowing that there is still a small risk involved.
2. p = .01 (99% level): This level requires a higher standard of evidence to declare statistical significance. If the p-value is less than .01, you can conclude that the effect is statistically significant at a 99% confidence level. In terms of patient risk, this implies that there is only a 1% chance (or 1 in 100) that the observed effect is due to chance. Implementing decisions based on this level offers greater confidence in the results, indicating a lower risk associated with the findings.
3. p = .001 (99.9% level): This level represents an even higher standard of evidence. If the p-value is less than .001, you can conclude that the effect is statistically significant at a 99.9% confidence level. Patient risk is minimized with this level since there is only a 0.1% chance (or 1 in 1,000) that the observed effect is due to chance. Implementing decisions based on this level provides the highest level of confidence in the findings and significantly reduces the risk to patients.
It's essential to note that statistical significance alone does not guarantee the practical significance or the magnitude of the observed effect. Additionally, clinical judgment and other contextual factors should always be considered when implementing decisions that may affect patient risk.