This is for taking surveys. The Null hypothesis for the above experiment is that the observed values are close to the predicted values. The alternative hypothesis is that they are not close to the predicted values. These hypotheses hold for all Chi square goodness of fit tests. Thus in this case the null and alternative hypotheses corresponds to:Null hypothesis: The coin is fair Alternative hypothesis: The coin is biased The chisquare test for independence is used to determine the relationship between two variables of a sample. In this context independence means that the two factors are not related. Typically in social science research, we're interested in finding factors which are related, e.g. education and income, occupation and prestige, age and voting behaviour. Example: We want to know whether boys or girls get into trouble more often in school. Below is the table documenting the frequency of boys and girls who got into trouble in school H1 = There is no relationship Variables are Independent. H2 = There is a relationship Variables are dependent. If ChiSquare is greater than the 5% value in the table => H2 => 95% chance that there is signaficant relationship. Variables are dependent. Degrees of Freedom = (Row  1)*(Column 1) 
