1️⃣ Publication Bias – The “Invisible” Studies

Many meta-analyses only include published studies, leaving out negative or unpublished results. This skews conclusions.
👉 Example: In antidepressant trials, studies with less favorable results often remain unpublished, leading to an overestimation of effectiveness.

2️⃣ Mixing Apples and Oranges – Heterogeneity Problems

Combining studies that are too different in design, population, or intervention can create misleading pooled results.
👉 Example: Pooling trials of adults and children in asthma treatment without subgroup analysis could mask true age-related effects.

3️⃣ Poor Quality In, Poor Quality Out

Including low-quality or high-bias studies can weaken the validity of the meta-analysis. Using standardized tools (e.g., Cochrane RoB tool) helps filter these out.
👉 Example: A meta-analysis on herbal supplements for diabetes included small, poorly designed studies and exaggerated the benefits.

4️⃣ Selective Outcome Reporting

Sometimes studies measure many outcomes but only report favorable ones. If not checked, this bias gets carried into the meta-analysis.
👉 Example: In weight-loss interventions, studies may report body weight but ignore metabolic markers that showed no improvement.

5️⃣ Misinterpreting Statistical Significance

A significant pooled effect doesn’t always mean clinical importance. Effect size and confidence intervals must be considered.
👉 Example: A blood pressure reduction of 2 mmHg may be statistically significant but not clinically meaningful for patient outcomes.

💡 Final Note: High-quality meta-analyses use comprehensive searches, clear criteria, quality checks, and sensitivity analyses to avoid these pitfalls.