Many of these approximations were quite useful when computers did not exist, but now that taking a log is really no more vexing than multiplying two numbers, other approximations may be more useful, especially in special cases were the approximations are suspect.
A statistical model is often a parametrized family of probability density functions or probability mass funtions f_{θ}(x). A null hypothesis is often stated by saying the parameter θ is in a specified subset Θ_{0} of the parameter space Θ. The likelihood function is L(θ) = L(θ x) = f_{θ}(x) = a function of the parameter θ with x held fixed at the value that was actually observed, i.e., the data. The likelihood ratio is
If the null hypothesis is true, then 2 log Λ will be asymptotically χ^{2} distributed with degrees of freedom equal to the difference in dimensionality of Θ and Θ_{0}.
For instance, in the case of Pearson's test, we might try to compare two coins to determine whether they have the same probability of coming up heads. Our observation can be put into a contingency table with rows corresponding to the coin and columns corresponding to heads or tails. The elements of the contingency table will be the number of times the coin for that row came up heads or tails. The contents of this table are our observation X.
Heads  Tails  
Coin 1  k_{1H}  k_{1T} 
Coin 2  k_{2H}  k_{2T} 
The hypothesis and null hypothesis can be rewritten slightly so that they satisfy the constraints for the loglikelihood ratio to have the desired nice distribution. Since the constraint causes the twodimensional H to be reduced to the onedimensional H_{0}, the asymptotic distribution for the test will be χ^{2}(1), the χ^{2} distribution with one degree of freedom.
For the general contingency table, we can write the loglikelihood ratio statistic as
<math>2 \log \Lambda = \sum_{i,j} k_{ij} \log {p_{ij} \over m_{ij}} </math>
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