** Chi- squared is also know as the goodness of fit, this means that it tests the shape and proportion of the data in relation to the null. the goodness of fit if tested by comparing the observed frequencies with the distribution set in the null hypothesis. **

**chi squared in my eyes is the most understandable of all the statistical tests as there is no need to calculate sample means you just use calculation of individuals in each category . this is a result of the observed frequency ; the number of individuals in each category.**

**I believe tha chi- squared is one of the most helpful statistical tests as it is able to test the relationship between to variables thus enabling causality. this is done by evaluating the between the two variables. **

**David schoenfeild conducted an experiment using this statistical test. these tests were based both expected and observed frequencies and whether these covariets fall within the hypothetical L. in this study similarly to clinical trails there had the be partitions within the observable data in order to tell significance . here chi squared helped as it tested the individual components of the data in order for a correlation between Cox’s proposed model and the for the proportional hazards regression model .**

http://biomet.oxfordjournals.org/content/67/1/145.short

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I do agree that chi squared is really useful especially because it can show causality. It is really hard to prove causality, and people often think that correlation can show it, but this isn’t the case and we do need a way of proving causality. I also think that Chi squared is useful because it can compare the mean between to different samples. This would allow somebody to be able to compare the differences between on population and another. I think that this is important as it is a statistical way in which to show the differences between populations, which could explain why the populations are so different.