Nurses should be aware of common pitfalls in hypothesis testing, such as:
Type I Error: This occurs when the null hypothesis is incorrectly rejected. It is akin to a false positive. Type II Error: This happens when the null hypothesis is not rejected when it should be. It is similar to a false negative. Sample Size: A small sample size may not provide enough power to detect a significant effect, while a very large sample may detect trivial differences. Misinterpretation of Results: Statistical significance does not imply clinical significance. Nurses must consider the practical implications of their findings.
