Statistical power refers to the probability that a statistical test will correctly reject a false null hypothesis. It is a crucial aspect of experimental design, as it determines the likelihood of detecting an effect when there is one. In this article, we present three diverse examples of Statistical Power Lab Reports to illustrate its application across different fields.
In a clinical trial setting, researchers aim to determine whether a new medication significantly lowers blood pressure compared to a placebo. The context involves a sample of 100 participants, with 50 receiving the medication and 50 receiving the placebo. The researchers aim for a statistical power of 0.80, meaning there is an 80% chance of detecting a true effect.
To compute the statistical power, the researchers use the following parameters:
Using a power analysis software tool, they find that with these parameters, the statistical power is approximately 0.82. This indicates that their sample size is adequate to detect a significant difference in blood pressure if the new drug is effective.
Notes: In this scenario, researchers could consider varying the sample size or significance level to explore how these changes affect power. For instance, increasing the sample size to 200 participants would likely increase power further, allowing for more robust conclusions.
An educational researcher wants to evaluate the effectiveness of a new teaching method on student performance. The study involves two groups of students: one group using traditional teaching methods and the other using the new method. The researcher hypothesizes that the new method will lead to higher test scores.
Key parameters for the study include:
Conducting a power analysis, the researcher determines that the statistical power for this setup is 0.75, which is somewhat lower than the desired threshold of 0.80. To improve power, the researcher considers increasing the sample size to 80 students total, which would increase the power to approximately 0.85.
Notes: This example illustrates the importance of effect size in power calculations. A larger effect size can allow for smaller sample sizes while still achieving adequate power. Researchers should always aim for a balance between feasibility and statistical rigor in their studies.
A public health study is designed to investigate whether a 12-week exercise program leads to significant weight loss among participants. The researchers want to ensure that their study has enough power to detect a meaningful difference in weight loss between the exercise group and a control group that does not participate in the program.
For this study, the parameters include:
After conducting a power analysis, the researchers find that their study has a statistical power of 0.79. To increase this power to the desired level of 0.85, the researchers decide to recruit an additional 20 participants, bringing the total to 140.
Notes: This example demonstrates how researchers can adjust their sample sizes based on initial power calculations to ensure adequate power for detecting significant effects. Monitoring the performance of the study over time can also inform adjustments to sample size or study design.