BACKGROUND: Numerous assumptions and techniques are associated with performing meta-analysis.  While some overall structural guidelines and recommended practices exist, there are very few papers that compare meta-analysis techniques in application. OBJECTIVES: To review primary meta-analysis methods and their assumptions, and apply various meta techniques to data and compare the results. METHODS:   There are currently a myriad of meta-analysis techniques available.  We started the study with a review of fixed effects models, which is the most basic technique that assumes homogeneity in treatment effect across studies. We then explored random effect models and meta regression. Each of these techniques models treatment heterogeneity.  Other more advanced techniques examined included mixed treatment comparisons (MTC) and Bayesian approaches. RESULTS: Estimates of treatment effect differed depending on the meta technique applied.  When a fixed effect model was applied to estimate the effect of a vaccination against tuberculosis, the log odds ratio was -0.436 (confidence interval [CI: -0.528, -0.344]).  After testing for heterogeneity and fitting a random effects model, the estimate was reduced to -0.741 (CI [-1.12, -0.352]), and the CI became wider.  When covariates were added to the model to explain the heterogeneity, the treatment effect was reduced even further.  Additional techniques were applied as well, such as Bayesian MTC.  CONCLUSIONS: Results from meta-analysis are sensitive to the studies selected, in addition to the methodology applied.  To ensure that proper techniques are used, it is critical to estimate an unbiased outcome.