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?
The smaller the p value, the less likely that the results were due to chance and the more likely the results will be effective. When you replicate the results with more experiments, this reduces p even further.
Suppose two experiments found significance at P = .001. The probability that both would have occurred by chance = .001 * .001 = .000001.
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?
When evaluating the risk to patients in implementing decisions based on research findings, the three levels of statistical significance (p = .05, p = .01, and p = .001) can provide valuable guidance. Here's how you can use these levels in relation to evaluating the risk to patients:
1. p = .05 (95% level of significance):
This level is commonly used in many fields as a standard threshold for statistical significance. If a research finding is statistically significant at p = .05, it means that there is a 5% chance that the observed effect is due to random chance or variability in the data. In the context of patient risk, a finding at this level suggests that the implemented decision has a relatively low probability of being a false positive. However, it still leaves a small room for error, so cautious consideration should be given to the potential risks involved in implementing the decision.
2. p = .01 (99% level of significance):
At this higher level of significance, the threshold for accepting a research finding as statistically significant is more stringent. If a result is significant at p = .01, it means there is only a 1% chance that the observed effect is due to random chance. In terms of patient risk, a finding at this level provides stronger evidence that the decision based on the research findings is likely to be accurate and reliable. The risk associated with implementing the decision is further reduced compared to the p = .05 level.
3. p = .001 (99.9% level of significance):
This is the highest level of significance commonly used, representing a very stringent threshold. If a research finding is significant at p = .001, it means there is only a 0.1% chance that the observed effect is due to random chance. In the context of patient risk, this level provides the strongest evidence that the decision based on the research findings is accurate and reliable, with an extremely low probability of being a false positive. Implementing the decision based on such highly significant findings carries minimal risk to patients.
In summary, the three levels of significance can be used to evaluate the risk to patients in implementing decisions based on research findings. Higher levels of significance (e.g., p = .01 or p = .001) provide stronger evidence and reduce the risk associated with implementing the decision. However, it is important to also consider other factors such as sample size, effect size, and potential biases when evaluating the overall risk to patients.