Changes

:Watch how evolutionists defended obvious errors in the Lenski paper, and then realize why the [[Piltdown Man]] fraud was taught for 40 years without evolutionists admitting it was a hoax.--[[User:Aschlafly|Andy Schlafly]] 09:55, 5 March 2009 (EST)

:Watch how evolutionists defended obvious errors in the Lenski paper, and then realize why the [[Piltdown Man]] fraud was taught for 40 years without evolutionists admitting it was a hoax.--[[User:Aschlafly|Andy Schlafly]] 09:55, 5 March 2009 (EST)

Sjohnson, I believe you just proved my point.  In the literature of mean and covariance structure analysis, non-central chi-square distribution is commonly used to describe the behavior of the likelihood ratio statistic under alternative hypothesis; it is widely believed that the non-central chi-square distribution is justified by statistical theory. Actually, when the null hypothesis is not trivially violated, the non-central chi-square distribution cannot describe the LR statistic well even when data are normally distributed and the sample size is large. Monte Carlo results compare the strength of the normal distribution against that of the non-central chi-square distribution.  In an association analysis comparing cases and controls with respect to allele frequencies at a highly polymorphic locus, a potential problem is that the conventional chi-squared test may not be valid for a large, sparse contingency table. Reliance on statistics with known asymptotic distribution is unnecessary, as Monte Carlo simulations can be performed to estimate the significance level of the test statistic.

Sjohnson, I believe you just proved my point.  In the literature of mean and covariance structure analysis, non-central chi-square distribution is commonly used to describe the behavior of the likelihood ratio statistic under alternative hypothesis; it is widely believed that the non-central chi-square distribution is justified by statistical theory. Actually, when the null hypothesis is not trivially violated, the non-central chi-square distribution cannot describe the LR statistic well even when data are normally distributed and the sample size is large. Monte Carlo results compare the strength of the normal distribution against that of the non-central chi-square distribution.  In an association analysis comparing cases and controls with respect to allele frequencies at a highly polymorphic locus, a potential problem is that the conventional chi-squared test may not be valid for a large, sparse contingency table. Reliance on statistics with known asymptotic distribution is unnecessary, as Monte Carlo simulations can be performed to estimate the significance level of the test statistic.