Pooling five studies with different outcome measures produces a number nobody can interpret

Popularity Index: 3 Updated at 2026-10-11 14:37 UTC

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Random effects and fixed effect models answer different questions and the flattering one gets reported Dropping the one enormous trial in a sensitivity analysis usually decides the pooled result A forest plot with I-squared at 85 percent is an average of studies that do not belong together Risk of bias tools score reporting quality rather than whether the study was actually done well Web logs undercount readers once browsers clear cookies and block scripts Non-inferiority margin: choosing it from historical data instead of convenience Registering the protocol before the search starts is what stops the question from quietly changing Acquiescence bias and why a grid of agree statements inflates every score Permutation tests as the escape route when the normality assumption fails Testing an interaction term is not the same as testing the simple effects

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