Difference between revisions of "Significance of E. Coli Evolution Experiments"

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The chi-square test is a common statistical method.<ref>''Mathematical Statistics with Applications'' by Wackerly, Mendenhall, and Scheaffer, Section 14.4.</ref> It can be implemented in Microsoft Excel. If the numbers from the last four columns of the table above (excluding the “totals” row) are entered into Excel in rows 1-12 and columns A-D, then the p-value can be computed by entering “=CHITEST(A1:B12,C1:D12)” into any empty cell of the spreadsheet.
 
The chi-square test is a common statistical method.<ref>''Mathematical Statistics with Applications'' by Wackerly, Mendenhall, and Scheaffer, Section 14.4.</ref> It can be implemented in Microsoft Excel. If the numbers from the last four columns of the table above (excluding the “totals” row) are entered into Excel in rows 1-12 and columns A-D, then the p-value can be computed by entering “=CHITEST(A1:B12,C1:D12)” into any empty cell of the spreadsheet.
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==Comparison of p-Values==
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The following table compares the p-values reported in Table 2 of Blount et al. to the chi-square p-values for the same experiments. For experiments one and three, the chi-square p-values are much larger than the "mean generation" test p-values from the paper.
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{|class="wikitable" style="text-align:center"
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|-
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|
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!Experiment 1
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!Experiment 2
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!Experiment 3
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|-
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!p-Value from Paper
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|0.0085
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|0.0007
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|0.082
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|-
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!Chi-square p-value
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|0.19
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|0.0004
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|0.22
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|}
  
 
==References==
 
==References==
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[[Category:Conservapedia Dealings with PNAS and Lenski]]
 
[[Category:Conservapedia Dealings with PNAS and Lenski]]
 
[[Category:Statistics]]
 
[[Category:Statistics]]
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==See Also==
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http://www.sciencenews.org/index/feature/activity/view/id/40006/title/Molecular_Evolution
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http://sciencenews.org/view/generic/id/40649/title/FOR_KIDS_Hitting_the_redo_button_on_evolution

Revision as of 14:10, March 5, 2009

Blount, Borland, and Lenski[1] claimed that a key evolutionary innovation was observed during a laboratory experiment. That claim is false. The claim was based on incorrect measurements of statistical significance. Rather than using a test from the statistics literature, a flawed test was contrived and used to measure significance. The flawed test (“mean mutation generation”) produced artificially low p-values.

The data from experiment one of the paper is shown below (see Table 1 of the paper). The expected outcomes under the null hypothesis (no evolutionary innovation occurs) are also shown.

Generation Trials Mutants Statics Expected Mutants Expected Statics
0 6 0 6 0.333 5.667
10000 6 0 6 0.333 5.667
20000 6 0 6 0.333 5.667
25000 6 0 6 0.333 5.667
27500 6 0 6 0.333 5.667
29000 6 0 6 0.333 5.667
30000 6 0 6 0.333 5.667
30500 6 1 5 0.333 5.667
31000 6 0 6 0.333 5.667
31500 6 1 5 0.333 5.667
32000 6 0 6 0.333 5.667
32500 6 2 4 0.333 5.667
Total 72 4 68 4 68

When the flawed test is used to compute the significance of this data, the p-value is 0.0085 (see Table 2 of the paper). This p-value is considered statistically significant. However, when the data is analyzed using a standard method (the chi-square test) the p-value is 0.19. This p-value is much larger than the one from the paper and indicates that there is no reason to reject the null hypothesis. The chi-square test p-value for experiment two is small (0.0004). However, experiment three is not statistically significant because its p-value is 0.22.

The chi-square test is a common statistical method.[2] It can be implemented in Microsoft Excel. If the numbers from the last four columns of the table above (excluding the “totals” row) are entered into Excel in rows 1-12 and columns A-D, then the p-value can be computed by entering “=CHITEST(A1:B12,C1:D12)” into any empty cell of the spreadsheet.

Comparison of p-Values

The following table compares the p-values reported in Table 2 of Blount et al. to the chi-square p-values for the same experiments. For experiments one and three, the chi-square p-values are much larger than the "mean generation" test p-values from the paper.

Experiment 1 Experiment 2 Experiment 3
p-Value from Paper 0.0085 0.0007 0.082
Chi-square p-value 0.19 0.0004 0.22

References

  1. ↑ http://www.pnas.org/content/105/23/7899.full.pdf
  2. ↑ Mathematical Statistics with Applications by Wackerly, Mendenhall, and Scheaffer, Section 14.4.

See Also

http://www.sciencenews.org/index/feature/activity/view/id/40006/title/Molecular_Evolution http://sciencenews.org/view/generic/id/40649/title/FOR_KIDS_Hitting_the_redo_button_on_evolution

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