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Talk:Significance of E. Coli Evolution Experiments (view source)
Revision as of 15:27, March 5, 2009
, 15:27, March 5, 2009Reply to SJohnson
: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.
Here is a [http://faculty.vassar.edu/lowry/chi_beta.html link] to a great page the provides an interactive example as to why the Chi Squared test would provide poor results compared to the Monte Carlo in relation to the Lenski data workup.
Something you may have overlooked was that the data set is actually too small to use the chi square method correctly. It is often accepted that is any of the analyzed data falls under 10 for a particular cell of the data set then the Yates correction needs to be applied; unfortunately the Yates correction can over correct thus skewing the p-value. Lenksi seemed to understand this by supporting his Monte Carlo p-value results with the Fisher z-transformation p-value.
I hope this helps.--[[User:Able806|Able806]] 10:27, 5 March 2009 (EST)