Considering that a researcher can test at the <p = .05 (95%), p = .01 (99%), or p = .001 (99.9%) level for statistical significance, how do you use these three levels in relationship to evaluate risk to patients in implementing the decisions based on research findings?
When evaluating the risk to patients in implementing decisions based on research findings, the significance levels (also known as alpha levels) play an important role in statistical hypothesis testing. These levels help researchers determine the appropriate threshold for considering an effect statistically significant.
The three commonly used significance levels are:
1. p = .05 (95% level): This level is widely used in many fields of research. It means that there is a 5% chance of observing the obtained results if the null hypothesis (the hypothesis that there is no effect) is true. If the p-value calculated from the data is less than .05, researchers can reject the null hypothesis and conclude that there is a statistically significant effect. In terms of risk to patients, this level suggests a moderate level of certainty in the research findings.
2. p = .01 (99% level): This level provides a higher threshold for rejecting the null hypothesis. It means that there is only a 1% chance of observing the obtained results if the null hypothesis is true. If the p-value is less than .01, researchers can conclude that the effect is statistically significant at a higher confidence level. From a risk perspective, this level indicates higher confidence in the research findings, which may lead to more cautious and conservative decisions in patient care.
3. p = .001 (99.9% level): This level represents the highest threshold for statistical significance. It means that there is only a 0.1% chance of observing the obtained results if the null hypothesis is true. If the p-value is less than .001, researchers can confidently claim that the effect is statistically significant at a very high confidence level. In terms of patient risk, this level implies a greater degree of certainty in the research findings, which may lead to more conservative and stringent implementation decisions to minimize potential risks to patients.
In summary, the significance levels provide researchers with a framework to evaluate the strength of evidence supporting research findings. Higher significance levels (e.g., p = .01 or p = .001) indicate greater confidence in the results but may also lead to more conservative decision-making to manage patient risk effectively. The choice of significance level depends on the specific context, the potential consequences of the decisions, and the acceptable level of risk in patient care.