When combining the outcomes from different studies, one may use a fixed and/or random effects model. A fixed effects model assumes that all the studies share the same common treatment effect while a random effects model assumes that they do not share the same common treatment effect. In the absence of significant heterogeneity, which is often calculated using what is known as the Q statistic, both the fixed and random effects models will yield similar confidence intervals. However, if significant heterogeneity is present, the random effects model will yield wider confidence intervals. Recent research suggests that the random effects model is preferable to the fixed effects model (See: Hunter JE, Schmidt FL. Fixed effects vs. random effects meta-analysis models: implications for cumulative research knowledge. International Journal of Selection and Assessment 8:275-292, 2000).