Difference between revisions of "Significance of E. Coli Evolution Experiments"
(Created page about the incorrect p-values in the E. Coli evolution paper) |
m (cats) |
||
| Line 110: | Line 110: | ||
==References== | ==References== | ||
<references/> | <references/> | ||
| + | [[Category:Conservapedia Dealings with PNAS and Lenski]] | ||
| + | [[Category:Statistics]] | ||
Revision as of 22:40, March 4, 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.
References
- ↑ http://www.pnas.org/content/105/23/7899.full.pdf
- ↑ Mathematical Statistics with Applications by Wackerly, Mendenhall, and Scheaffer, Section 14.4.
