Meta-Analysis refers to the statistical procedure for collaborating data from different studies based on a particular topic. Meta-analysis is used to study and identify the common effect of a particularly same treatment, wherein the effect of the treatment remains consistent through multiple cases of study. Meta-analysis performs a crucial role in evidence-based health care. In comparison to other study designs like cohort studies or randomized controlled trails, Meta-analysis is placed at the top of ‘levels of evidence’ in the pyramid of evidence-based healthcare. The pyramid particularly helps analyze the different levels of evidence that are available. As one proceeds to the top from every level of evidence in the evidence pyramid, the evidence becomes less biased as compared to the previous level. Thus, Meta-analysis can be regarded as the apex of healthcare evidence (Haidich, 2010). Meta-analyses began to appear as a leading part of research in the late 70s. Since then, they have become a common way for synthesizing evidence and summarizing the results of individual studies (Chalmers et al., 1977). Pubrica has extensive experience in conducting meta-analysis, a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research. As a general principle, generating, summarizing, and understanding the best available evidence are essential for establishing the benefits and safety of interventions. A well-designed and implemented meta-analysis study can be a great benefit to society. While meta-analysis has the potential to be a powerful tool in evaluating healthcare treatments and interventions, there are many potential pitfalls and problems that are yet to be resolved. A poorly performed meta-analysis can perpetuate biases from ill-conceived studies or lead to false conclusions. This, in turn, can cause consumers and caregivers, who frequently access results of meta-analysis through websites and popular press, to form incorrect conclusions and can result in inappropriate medical decisions. While the idea behind combining studies to improve precision and power is straightforward, the actual implementation of the process is difficult. Those who act or react based on meta-analysis should understand the various biases that could be incorporated into a review. Examples of some potential pitfalls in meta-analysis include publication bias, pipeline bias, and English language bias. In addition, combining studies that are not similar in study design, population, methods of analysis or outcome definitions can lead to biases as well, which may result in spurious conclusions being drawn. Our Meta-analysis Writing Services: Guidelines for Reporting:Moreover, a useful guide to improve reporting of systematic reviews and meta-analysis is the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analysis) statement that replaced the QUOROM (Quality of Reporting of Meta-analysis) statement. Detailed Literature Search: We include all relevant studies,because loss of studies can lead to bias in the study. Typically, published papers and abstracts are identified by a computerized literature search of electronic databases that can include PubMed (http://www.ncbi.nlm.nih.gov./entrez/query.fcgi), ScienceDirect (http://www.sciencedirect.com/), Sciurus (http://www.scirus.com/srsapp), ISI Web of Knowledge (https://www.isiwebofknowledge.com/), Google Scholar (https://scholar.google.com/) and CENTRAL(Cochrane Central Register of Controlled Trials, (https://www.mrw.interscience.wiley.com/cochrane/cochrane_clcentral_articles_fs.htm). The Need Of Performing Meta-Analysis: The validity of hypothesis cannot be based on outcomes of a single study. The results keep varying from one study to another. The results can differ due to multiple reasons like different study samples used and, confounding factors. Therefore, a mechanism is need for synthesizing data across multiple studies. Narrative reviews are used for this purpose. The major drawbacks associated with narrative reviews is that these reviews are largely subjective, i.e., different experts come to different conclusions based on their methods of study and results. It becomes more difficult when multiple studies are involved. Meta-analysis on the contrary makes use of objective formulas, i.e., apply statistics to a single study. This method can be used for multiple studies. The combination of different studies which indirectly provide more data can prove beneficial and also help in improving and achieving precision and accuracy in individual studies. Alternately, if the individual studies are under powered, their combination in a meta-analysis can improve the total statistical power used to detect a particular effect. Statistical Analysis: Heterogeneity: Heterogeneity (variation in true effect sizes and in factors that might influence those effect sizes) is inherent in meta-analysis, not a problem to be solved. It includes clinical components (e.g., diversity in patient populations or interventions) and statistical components (e.g., random differences).