# Are There Seven Symbols for the Nucleotide-Based Genetic Code?
## 3 Results
### The Genomic Texts Analysis
#### 3.1.3 Distribution Comparison of Genomic Texts  
Because there are many objections in the literature questioning the plausibility of the power-law distribution, additional tests were undertaken to compare the power-law distribution with its competitors. From all 780 analyses (60 genomes \(\times\) 13 analysis types), only 354 passed the power-law requirements stated by the K-S test. These corpora were further compared to the lognormal, the exponential, and the truncated power-law distribution. Figure 5 represents the results of this pair-distribution comparison.  
Figure 5: The distribution comparison of the genomic texts. The graphs represent the summary of three pair-distribution comparisons. The upper part of the figure shows the power law vs. the lognormal test, the middle part the power law vs. the exponential test, and the lower section the power law vs. the truncated power law. The horizontal bars represent the number of analyzed texts that favor the particular distribution (i.e., texts with a statistically significant log-likelihood ratio score), and the colors of that bars are assigned accordingly: red indicates the lognormal distribution, blue the power-law distribution, and orange the truncated power-law distribution. The graphs should be read in the following manner: select the desire pair-distribution comparison (from the right bar) and within it the method analysis (from the left bar). The horizontal bars in the same row show the results. The blue bar expanding on the right side shows texts that favor the power law, whereas the bars in other colors expanding on the left represent texts that favor the competitive distribution. The lack of the analysis type, or the absence of the bar within a row, means that none of the text passed the K-S test, or there were no texts that favored this particular distribution.  
Comparing the power law vs. the lognormal distribution, we observed that the power law was not favored in any text, whereas the lognormal distribution was preferred in 55 cases (frame tokenization: 19 \(\times\) SOTFAC, 17 \(\times\) SCTFAC, 7 \(\times\) SNTFAC, 6 \(\times\) SOTFAN, 1 \(\times\) SNTFAN, and contextual tokenization 5 \(\times\) SOTCAC). The rest of the analyzed texts returned statistically irrelevant results. A much clearer situation was with the power law vs. the exponential test. Here, all 354 analyses that passed the K-S statistics exhibited the power law. The last case compares the power law with the truncated power law. The first of these two was not selected, whereas the truncated power law was favored in 313 of 354 cases (57 \(\times\) SOTFAC, 57 \(\times\) SCTFAC, 56 \(\times\) SOTFAN, 53 \(\times\) SNTFAC, 52 \(\times\) SCTCAC, 31 \(\times\) SNTFAN, 7 \(\times\) SOTACA).  
The statistical tools recommended in the literature for the power-law comparison were not entirely satisfying. Although it improved the K-S outcome by eliminating some analyses that went better with the logarithmic distribution, for the majority of texts the method did not provide us with statistically relevant results.