Milagros,
The problem that I see is that you are making subjective judgements from your own personal data set. That is, you are making off the cuff decisions from what you know. You like to call this probability, but in fact it is a personal estimation.
To use your example of a person jumping off of a 10 story building, it is a good estimation that most people will die on or directly after impact. However, there are examples of skydivers surviving a fall from an airplane at 10,000 feet. So how do we get the actual probability of survivors from a fall of 100 feet (10 feet/floor)? As ghastly as it sounds, the best way is to push 10,000 people from a 10 story building and see how many survive. After you collect this data, then you move from an estimate to a probability.
Drug companies follow the same technique when testing new drugs. Their study groups can differ between a few thousand to a few hundred thousand. They measure the number of people who recieve a benefit, no benefit, or side effects from the drug in question. This is then compared to the number of people in the placebo group reporting the same effects. These probabilities are then compared to one another to see if the drug is more effective than placebo without a high occurence of side effects. This same study can't be done by observing a few people, or by anecdotal evidence. You seem to be relying on just this sort of data, a few personal observations and anecdotal evidence.
Thirdly, beneficial mutations have been observed. Perhaps you can show us how beneficial mutations can occur but not enough of them do? Remember that we have only known about DNA for about 50 years, much less genes.
quote:
If you've calculated that 3 billion "beneficial" mutations may have occurred, what's that make the average of "beneficial" mutations occurring per year, decade, century, etc.? Now take that number and see if there is any scientific data that confirms this average. So that if you say every 6 years one "beneficial" mutation occurs, we can test this by seeing if this has been observed anywhere at anytime.
Molecular clocks are still a hot topic within evolutionary biology. The theory is that by taking the average mutation rate of a species you can back calculate to find common ancestory between two species. This has been done with fruit flies with decent success. By using the average mutation rate they were able to corroborate speciation with what fossil evidence there is for fruit fly evolution and speciation. From
http://www.pubmed.com:
Mol Biol Evol. 2004 Jan;21(1):36-44. Epub 2003 Aug 29.
Temporal patterns of fruit fly (Drosophila) evolution revealed by mutation clocks.
Tamura K, Subramanian S, Kumar S.
Center for Evolutionary Functional Genomics, Arizona Biodesign Institute, and School of Life Sciences, Arizona State University, USA.
Drosophila melanogaster has been a canonical model organism to study genetics, development, behavior, physiology, evolution, and population genetics for nearly a century. Despite this emphasis and the completion of its nuclear genome sequence, the timing of major speciation events leading to the origin of this fruit fly remain elusive because of the paucity of extensive fossil records and biogeographic data. Use of molecular clocks as an alternative has been fraught with non-clock-like accumulation of nucleotide and amino-acid substitutions. Here we present a novel methodology in which genomic mutation distances are used to overcome these limitations and to make use of all available gene sequence data for constructing a fruit fly molecular time scale.
Our analysis of 2977 pairwise sequence comparisons from 176 nuclear genes reveals a long-term fruit fly mutation clock ticking at a rate of 11.1 mutations per kilobase pair per Myr. Genomic mutation clock-based timings of the landmark speciation events leading to the evolution of D. melanogaster show that it shared most recent common ancestry 5.4 MYA with D. simulans, 12.6 MYA with D. erecta+D. orena, 12.8 MYA with D. yakuba+D. teisseri, 35.6 MYA with the takahashii subgroup, 41.3 MYA with the montium subgroup, 44.2 MYA with the ananassae subgroup, 54.9 MYA with the obscura group, 62.2 MYA with the willistoni group, and 62.9 MYA with the subgenus Drosophila.
These and other estimates are compatible with those known from limited biogeographic and fossil records. The inferred temporal pattern of fruit fly evolution shows correspondence with the cooling patterns of paleoclimate changes and habitat fragmentation in the Cenozoic.
(emphasis mine).
The important bits are highlighted. By using the measured mutation rate it is possible to corroborate what evidence is in the fossil record. This seems to go against what you are saying, that there is not sufficient mutation to account for what we see in the fossil record. It seems that what you find improbable from a subjective point of view collapses when compared to objective, repeatable observations.
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