Non-significant results do not necessarily mean there is no effect. They might indicate insufficient statistical power, small sample sizes, or high variability in the data. It is essential to consider the context, study design, and potential sources of bias when interpreting non-significant findings. Researchers should avoid overstating these results and consider them as part of a broader body of evidence.
