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

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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.
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When the test used by Blount 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, implying that the rate of mutation increases over time. However, when the data is analyzed using a standard method (the chi-square test) the p-value is 0.19. This p-value suggests that the mutation rate is constant over time. Note, however, that the chi-squared test is conservative when any expected value is less than 1 and unreliable when, as in the present case, many expected values are less than 0.5 <ref>B.S. Everitt (1977) ''The Analysis of Contingency Tables''. Chapman & Hall.</ref>. Consequently, while the p-value of 0.19 appears to suggest there is no reason to reject the null hypothesis, it is in fact much too high and the results of the chi-squared test are unreliable.
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In statistical terms, ''conservative'' means that the p-value is higher than it ought to be, causing the experimenter to accept the null hypothesis when it should in fact be rejected.
  
 
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 experiment one data table (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 experiment one data table (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.

Revision as of 14:49, March 14, 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.

Experiment One Data

The data from experiment one of the paper is shown below (see Table 1 of the paper). The expected outcomes under the null hypothesis (rate of mutation is constant over time) 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 test used by Blount 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, implying that the rate of mutation increases over time. However, when the data is analyzed using a standard method (the chi-square test) the p-value is 0.19. This p-value suggests that the mutation rate is constant over time. Note, however, that the chi-squared test is conservative when any expected value is less than 1 and unreliable when, as in the present case, many expected values are less than 0.5 [2]. Consequently, while the p-value of 0.19 appears to suggest there is no reason to reject the null hypothesis, it is in fact much too high and the results of the chi-squared test are unreliable.

In statistical terms, conservative means that the p-value is higher than it ought to be, causing the experimenter to accept the null hypothesis when it should in fact be rejected.

The chi-square test is a common statistical method.[3] It can be implemented in Microsoft Excel. If the numbers from the last four columns of the experiment one data table (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.

Experiment Three Data

The experiment three data from Blount et al. is shown in the table below. The expected numbers of mutants under the null hypothesis (constant mutation rate) is also shown.

Generation Trials Mutants Statics Expected Mutants Expected Statics
0 200 0 200 0.571 199.429
10000 200 0 200 0.571 199.429
20000 200 0 200 0.571 199.429
25000 200 0 200 0.571 199.429
27500 200 2 198 0.571 199.429
29000 200 0 200 0.571 199.429
30000 200 2 198 0.571 199.429
30500 200 0 200 0.571 199.429
31000 200 0 200 0.571 199.429
31500 200 0 200 0.571 199.429
32000 200 1 199 0.571 199.429
32500 200 1 199 0.571 199.429
Total 2800 8 2792 8 2792

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. ↑ B.S. Everitt (1977) The Analysis of Contingency Tables. Chapman & Hall.
  3. ↑ 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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