Small Sample Size: Smaller sample sizes increase the variability of the results, making it harder to detect a true effect. Low Effect Size: When the difference between groups is small, it becomes more challenging to detect this difference statistically. Poor Measurement Tools: Inaccurate or unreliable measurement tools can introduce noise, obscuring the true effect. High Variability: High variability in the data can make it more difficult to identify a consistent effect. Confounding Variables: Uncontrolled confounding can mask the true relationship between the exposure and outcome.
