Casualty is not a single entity instead it is multifactorial. Often there is a confusion of correlation and causality, and it is thought that they can be used interchangeably which is not true. A correlation is merely an association between two variables whereas the causation refers to the concept that change in the values of one variable will lead to the change in the values of another variable (Russo & Williamson, 2007). Three conditions need to be fulfilled to establish a causal relationship which are covariation, temporal precedence, and control of extraneous variables (Nordenfelt & Lindahl, 2012). For example, if we want to see whether a health program has improved the health, the first thing is covariation in which the comparison group does not have that program. The second point which must be fulfilled is that there should be temporal precedence and refer to the example above the health must of poor in the absence of the program. The third point is to eliminate the other variables which might be hampering the overall effect. The relationship between two variables must be direct, and it should be evident that only the health program is improving the health. If all three condition are met then only, we can say that a program has caused the improvement in the health outcome.Statistical significance is the probability that the difference observed between two group is by chance. The statistical difference depends on the sample size and effect size. The effect is the magnitude of the difference between two groups (Sullivan & Feinn, 2012). Effect size is considered to be the primary finding of the studies because p-value or statistical difference tell whether there is a difference or not but effect size tells you how much the difference is between the two groups. Therefore, the effect size is essential for the decision making of continuing a program.ReferencesNordenfelt, L. Y., & Lindahl, B. I. B. (2012). Health, Disease, and Causal Explanations in Medicine. Springer Netherlands. Retrieved from https://books.google.com.pk/books?id=YLspBgAAQBAJRusso, F., & Williamson, J. (2007). Interpreting causality in the health sciences. International Studies in the Philosophy of Science, 21(2), 157–170.Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279–282.