Show pageBacklinksCite current pageExport to PDFBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. 🗑️ 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 garbage_out.txt Last modified: 2025/06/16 09:47by administrador