Most of time for dealing with uncertainty we want to understand causality associated with some pattern. Statistical tools are used to measure it and how to discover it. Understanding causality with statistical point of view is one of biggest challenge particularly in area of social sciences where experiments are often impossible and observational studies are the norm. Yet in introductory statistics correlation and coefficient of determination are discussed for dealing causality. Most of time we use the phrase “correlation is not causality." This denial makes interpretation of results through statistical tool more complex because our causality is deep rooted way of interpretation for understanding uncertainty. To better satisfy the interests of user of statistics we must emphasize causality more in teaching statistics. There are many things that can be taught about causality that are not discipline specific. Students should be taught how to detect the causal connotations of words and phrases. Students must be taught to be proactive in seeking alternative explanations for differences, ratios and correlations in observational studies. Students must be taught the causal differences between description, prediction and explanation. Statistics should be expanded to include causality in ways that are discipline independent and professionally appropriate. For understanding causality we can see http://en.wikipedia.org/wiki/Causality
