55
edits
Changes
Jump to navigation
Jump to search
Line 64:
Line 64: + + + + + + + +
Talk:Significance of E. Coli Evolution Experiments (view source)
Revision as of 01:10, March 9, 2009
, 01:10, March 9, 2009→Misinterpretation of test: new section
Able806, you made a good point about the contingency table cell frequencies being relatively low, but were wrong when you said ”the data set is actually too small to use the chi square method correctly”. In the low cell frequency case the chi-square test is still effective, but the null hypothesis distribution of the chi-square statistic starts to look less like the chi-square distribution. Thus, p-values calculated using the chi-square distribution may be a bit off. However, Monte Carlo p-values are always imperfect as well because it's impossible to generate an infinite number of random realizations. There are imperfections in p-values generated by analytic and Monte Carlo methods. However, low cell frequencies does not explain the >20x and >2.5x differences between chi-square p-values and p-values from the paper for experiments one and three. The reason for those huge differences was the use of the flawed test statistic (“mutation generation”) in the paper. [[User:SJohnson|SJohnson]] 16:38, 5 March 2009 (EST)
Able806, you made a good point about the contingency table cell frequencies being relatively low, but were wrong when you said ”the data set is actually too small to use the chi square method correctly”. In the low cell frequency case the chi-square test is still effective, but the null hypothesis distribution of the chi-square statistic starts to look less like the chi-square distribution. Thus, p-values calculated using the chi-square distribution may be a bit off. However, Monte Carlo p-values are always imperfect as well because it's impossible to generate an infinite number of random realizations. There are imperfections in p-values generated by analytic and Monte Carlo methods. However, low cell frequencies does not explain the >20x and >2.5x differences between chi-square p-values and p-values from the paper for experiments one and three. The reason for those huge differences was the use of the flawed test statistic (“mutation generation”) in the paper. [[User:SJohnson|SJohnson]] 16:38, 5 March 2009 (EST)
== Misinterpretation of test ==
SJohnson, Your analysis seems to misinterpret the test. You say the null hypothesis is no mutation. They see a mutation (4 mutations, in fact, in the data set you show) so the null hypothesis is disproved. That's perfectly simple.
I don't know what the "mean mutation generation" test is but applying a chi-squared test to this dataset tests if the mutations are evenly distributed throughout the generations. Your test says they are, so there's no strong evidence to suppose that mutations are likely to occur in one generation rather than another. in the series of tests. Blount's test says thay aren't, so it's more likely that the mutation will occur later in the series of tests.
But that point (the foregoing paragraph) has no bearing at all on what you say the null hypothesis is. The mutation appeared, so that means that they hypothesis that the mutation can't happen is disproved. Very simple. [[User:FredFerguson|FredFerguson]] 21:10, 8 March 2009 (EDT)