 Students, now we are going to see the third type of literature review which is called meta analysis. Meta analysis entirely different from the systematic literature review and scoping review. Let's see how it is different from both type of reviews. Meta analysis is a method for systematically combining pertinent qualitative and quantitative study data from several selected studies to develop a single conclusion that has greater statistical power. Meta analysis is different from systematic literature review and scoping review because what we do in this? In this, the different studies that we have conducted, the qualitative and quantitative inferences are drawn, we integrate those qualitative and quantitative inferences in one study and integrate it in one study and draw a journal inference from it. And we believe that the inference we are drawing in a collective study is more significant as compared to all those single studies. This conclusion is statistically stronger than the analysis of any single study due to increased number of subjects, greater diversity among subjects or accumulated effects and results. Because in this, as I told you earlier, we consider it to be more significant, so why is it more significant? Because in that, the analysis of your meta analysis, your sample size has increased, your sample units have increased and in that, your methodological analysis has become more rigorous in that. So on this basis, we consider that these results are more valid. So why do we do meta analysis? So on that, we see that what are the objectives of doing this? The first objective of this is to establish statistical significance with studies that have conflicting results. So here, if you look at the main thing, which is important, is that meta analysis is done in such situations that when you have conflicting results, one branch of studies supports a particular hypothesis, accepts it, whereas the second branch of studies rejects it or disapproves it. So in such situations, we conduct meta analysis so that all those studies are included in a single study and see what the results are. To develop a more correct estimate of effect magnitude. So in this, we try to follow our analysis in a way that is more significant as compared to earlier conducted studies. And the third objective of meta analysis is to provide a more complex analysis of farms, safety data and benefits. This analysis is more complex as compared to the single handedly conducted studies. And in meta analysis, we try to examine subgroups with individual numbers that are not statistically significant. So in a single study, it is possible that the results of subgroups are not statistically significant. But it is possible that when you include those studies in meta analysis, then that subgroup becomes significant for you. What are the advantages and disadvantages of meta analysis? The first advantage of meta analysis is greater statistical power. The results you draw from meta analysis are statistically stronger and their generalizability is better. The meta analysis is confirmatory data analysis, that is, its initial preliminary data analysis has been done and you are reassessing them in a way and confirming the last data analysis. For meta analysis, you have greater ability to extrapolate through general population affected. That is, the population in the study, you can see the high process conducted on it, whether it is conducted correctly or not. And you considered an evidence-based resource. Because you have multiple studies analysis, this is a more reliable evidence for you. Now let's look at the disadvantages of meta analysis. The disadvantage of meta analysis is that it is very difficult to do. And it is a time-consuming work to identify appropriate studies. If you add those studies to meta analysis, it needs some time. This is why it is a time-consuming work. And not all studies provide advocate data for inclusion and analysis. When you do meta analysis, we see that some studies, impact factor studies, some of your local studies, we cannot bring them to one level. And most of the local studies published are not their statistical regressness. So it becomes very difficult to include or not include anyone. And to do meta analysis, you require advanced statistical techniques. For this, you need to use scantry data analysis techniques and for that, you need advanced statistical knowledge. And the last point is heterogeneity of study populations. This is a hypothesis that is tested in different study populations. There will be some European societies, some Eastern societies, some American societies, some communities. So it is very difficult to control the cultural variation in meta analysis. So on the basis of all these things, we can see that meta analysis is a scientific work, but it is difficult to perform. And for this, you need a certain amount of skills to perform the meta analysis.