This has nice explanations of the usual (random selection of participants, attrition, observer bias, etc.) plus one I've never heard of: "funnel plots" for evaluating meta-analysis:
There’s bias in meta-analyses, too. In fact, a significant percentage of meta-analyses are biased and their findings are later contradicted in large randomized controlled trials, according to Drs. Matthias Egger, M.D., and George Davey Smith, M.D., D.Sc., at the University of Bristol, UK, in a 1995 issue of the British Medical Journal.
“Funnel plots” can help to identify bias in meta-analyses. In making a funnel plot, the estimated size of treatment effects in the studies used are plotted against the sample size. They’re based on the fact that precision in estimating the treatment effect increases as the sample size of component studies increases. Results from small studies will scatter widely at the bottom of the graph, with the spread narrowing among larger studies at the top. “If there is no publication bias, the plot should resemble a symmetrical inverted funnel [or pyramid],” they said. [Image from: Antioxidant supplements for prevention of mortality in healthy participants and patients with various diseases. Cochrane Database of Systematic Reviews 2008]