🗑️ Garbage in, garbage out (GIGO) – Definition: A principle from computer science and data analysis meaning that if the input data is flawed, biased, inconsistent, or poorly defined, then the output—no matter how sophisticated the analysis—will also be unreliable or meaningless.

In clinical research (especially meta-analysis), this refers to:

Including studies with poor methodology

Combining heterogeneous populations

Using inconsistent definitions of outcomes

🧠 Applied to meta-analysis: No statistical model can compensate for flawed or incompatible source data. Pooled nonsense remains nonsense.

✂️ “Garbage in” = poorly selected studies 🧮 “Garbage out” = misleading pooled effect sizes and false conclusions