- Assumptions: Confidence intervals rely on certain assumptions (e.g., normal distribution of data). If these assumptions are violated, the interval may be inaccurate. - Sample Quality: Poor-quality or biased samples can lead to misleading confidence intervals. - Interpretation Complexity: Misinterpretations can occur, especially among those not well-versed in statistical concepts.
