Deep problem created by Darwinian Ron Fisher’s p-values highlighted again

Bob O'H:

R.A. Fisher was also a Christian, so can we blame Christianity of p-values too?

I think we can blame Christianity for Darwinism.Mung

February 19, 2017

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"I" think it is clear enough. And since atheistic materialism can not ground "I" in the first place, then what "I" think carries more weight than what the neuronal illusion labeled Bob thinks. :) Fellow neuronal illusions may disagree! But why should "I", as a 'soul', expect anything else from randomly generated illusions that have no free will?

“You don’t have a soul. You are a soul. You have a body.” George MacDonald - Annals of a Quiet Neighborhood - 1892

bornagain77

February 19, 2017

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BA77 @ 3 -

“R.A. Fisher was also a Christian,” So?

Indeed. I don't see that it's any more relevant than Fisher's evolutionary work. Can you be a bit more explicit about your "corrective" than just giving a couple of Wikipedia links, please. It's not clear to me what exactly you are correcting.Bob O'H

February 19, 2017

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"R.A. Fisher was also a Christian," So? Many early supporters of Darwinian evolution were liberal Christians who, IMHO, were Christian in name only. Still today, Biologos is full of supposed Christians who, apart from their claim of being Christian, you cannot tell apart from full blown neo-Darwinists.

“There were also false prophets among the people, just as there will be false teachers among you.” (2 Peter 2:1)

Of corrective note to your post:

the distinction between Fisher's "significance testing" and Neyman-Pearson "hypothesis testing", and whether the likelihood principle should be followed. Some of these issues have been debated for up to 200 years without resolution.[1],,, https://en.wikipedia.org/wiki/Foundations_of_statistics https://en.wikipedia.org Fisher's "significance testing" vs Neyman-Pearson "hypothesis testing" Fisher popularized significance testing, primarily in two popular and highly influential books.,,, https://en.wikipedia.org/wiki/Foundations_of_statistics#Significance_testing

Of related note:

I seriously don’t think Darwinists should EVER talk about mathematics since,,, #1 they do not even pay attention to what their own mathematics from population genetics is telling them about the inadequacies of their own theory #2 Darwinists have no rigid mathematical basis to test against, as other overarching theories of science have, so as to qualify their theory as a science instead of a pseudo-science #3 The applicability of mathematics is itself a ‘miracle’ that is inexplicable to the materialistic presuppositions of Darwinists (August 2016) https://uncommondescent.com/intelligent-design/video-doug-axe-presents-the-thesis-of-his-new-and-fast-selling-book-undeniable/#comment-614428

bornagain77

February 19, 2017

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R.A. Fisher was also a Christian, so can we blame Christianity of p-values too? In reality, the way p-values are (mis-)used today owes more to Neyman & Pearson, who formalised significance testing. BA77 - Fisher wasn't a frequentist - his form of probability was much closer to the likelihood school. Incidentally, a change to Bayesianism or the likelihood school probably wouldn't change much. Likelihood tests still produce p-values, and Bayesians can do too - there are Bayesian p-values and Bayes Factors that can be mis-used just as badly.Bob O'H

February 19, 2017

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as to this quote from the article:

Statisticians should choose paradigms that solve the greatest number of real problems and have the fewest number of faults. This is why I believe that the Bayesian and likelihood paradigms should replace frequentist inference.

Two major contributors to frequentist (classical) methods were Fisher and Neyman.[4] Fisher's interpretation of probability was idiosyncratic (but strongly non-Bayesian). Neyman's views were rigorously frequentist.,,, Bayesian refers to methods in probability and statistics named after Thomas Bayes (c. 1702–61). Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief. It is interesting to note why Bayesian probability was developed:

How a Defense of Christianity Revolutionized Brain Science - JORDANA CEPELEWICZ ON DEC 20, 2016 Excerpt: Presbyterian reverend Thomas Bayes had no reason to suspect he’d make any lasting contribution to humankind.,,, in 1748,, philosopher David Hume published 'An Enquiry Concerning Human Understanding', calling into question, among other things, the existence of miracles. According to Hume, the probability of people inaccurately claiming that they’d seen Jesus’ resurrection far outweighed the probability that the event had occurred in the first place. This did not sit well with the reverend. Inspired to prove Hume wrong, Bayes tried to quantify the probability of an event.,,, “The basic probabilistic point” of Price’s article, says statistician and historian Stephen Stigler, “was that Hume underestimated the impact of there being a number of independent witnesses to a miracle, and that Bayes’ results showed how the multiplication of even fallible evidence could overwhelm the great improbability of an event and establish it as fact.” The statistics that grew out of Bayes and Price’s work became powerful enough to account for wide ranges of uncertainties. In medicine, Bayes’ theorem helps measure the relationship between diseases and possible causes. In battle, it narrows the field to locate an enemy’s position. In information theory, it can be applied to decrypt messages. And in the brain, it helps make sense of sensory input processes. http://nautil.us/blog/how-a-defense-of-christianity-revolutionized-brain-science

This following site, using Fisher's "significance testing", purported to show 'statistically significant' support for common ancestry:

29+ Evidences for Macroevolution – Douglas Theobald, Ph.D. Part 1: – The Unique Universal Phylogenetic Tree – Prediction 1.2: A nested hierarchy of species Excerpt: Seventy-five independent studies from different researchers, on different organisms and genes, with high values of CI (P less than 0.01) is an incredible confirmation with an astronomical degree of combined statistical significance (P less than less than 10-300, Bailey and Gribskov 1998; Fisher 1990). per Talk Origins under 'Potential Falsification' section: http://www.talkorigins.org/faqs/comdesc/section1.html#nested_hierarchy "How close must the measurements be in order to give a strong confirmation?" Scientists answer these questions quantitatively with probability and statistics (Box 1978; Fisher 1990; Wadsworth 1997). To be scientifically rigorous we require statistical significance. Some measurements of a given value match with statistical significance (good), and some do not (bad), even though no measurements match exactly (reality) So, how well do phylogenetic trees from morphological studies match the trees made from independent molecular studies? There are over 10^38 different possible ways to arrange the 30 major taxa represented in Figure 1 into a phylogenetic tree (see Table 1.3.1; Felsenstein 1982; Li 1997, p. 102).,,, under 'Confirmation' section http://www.talkorigins.org/faqs/comdesc/section1.html#independent_convergence

Interestingly, this following paper found 'highly significant' p-values even for contrasting phylogenetic hypothesis

Statistics and Truth in Phylogenomics - 2011 Excerpt: phylogenomics is becoming synonymous with evolutionary analysis of genome-scale and taxonomically densely sampled data sets. In phylogenetic inference applications, this translates into very large data sets that yield evolutionary and functional inferences with extremely small variances and high statistical confidence (P value). However, reports of highly significant P values are increasing even for contrasting phylogenetic hypotheses depending on the evolutionary model and inference method used, making it difficult to establish true relationships. http://mbe.oxfordjournals.org/content/29/2/457.full

The following site gives an overview of the many problems inherent for 'P values':

Scientific method: Statistical errors - P values, the 'gold standard' of statistical validity, are not as reliable as many scientists assume. - Regina Nuzzo - 12 February 2014 Excerpt: “P values are not doing their job, because they can't,” says Stephen Ziliak, an economist at Roosevelt University in Chicago, Illinois, and a frequent critic of the way statistics are used.,,, “Change your statistical philosophy and all of a sudden different things become important,” says Steven Goodman, a physician and statistician at Stanford. “Then 'laws' handed down from God are no longer handed down from God. They're actually handed down to us by ourselves, through the methodology we adopt.”,, One researcher suggested rechristening the methodology “statistical hypothesis inference testing”3, presumably for the acronym it would yield.,, The irony is that when UK statistician Ronald Fisher introduced the P value in the 1920s, he did not mean it to be a definitive test. He intended it simply as an informal way to judge whether evidence was significant in the old-fashioned sense: worthy of a second look. The idea was to run an experiment, then see if the results were consistent with what random chance might produce.,,, Neyman called some of Fisher's work mathematically “worse than useless”,,, “The P value was never meant to be used the way it's used today,” says Goodman.,,, The more implausible the hypothesis — telepathy, aliens, homeopathy — the greater the chance that an exciting finding is a false alarm, no matter what the P value is.,,, “It is almost impossible to drag authors away from their p-values, and the more zeroes after the decimal point, the harder people cling to them”11,, http://www.nature.com/news/scientific-method-statistical-errors-1.14700?WT.ec_id=NATURE-20140213

As to Theobald's 'statistical significance' paper, Casey Luskin responded thusly,,,

Douglas Theobald Tests Universal Common Ancestry by Refuting a Preposterous Null Hypothesis - Casey Luskin November 29, 2010 Excerpt: National Geographic notes in a subheadline: "Creationism called 'absolutely horrible hypothesis' -- statistically speaking." The problem is that Theobald didn't test universal common ancestry against "creationism." He tested universal common ancestry against the impossibly unlikely hypothesis that these genes independently arrived at highly similar sequences via blind, unguided convergent evolution. Given his outlandish null hypothesis, no wonder common descent came out looking so good. Again, if you don't believe me, consider what reviewers of a critique of Theobald's paper had to say (approving the critique!): Cogniscenti cringed when they saw the Theobald paper, knowing that "it is trivial". It is trivial because the straw man that Theobald attacks in a text largely formulated in convoluted legalese, is that significant sequence similarity might arise by chance as opposed to descent with modification. http://www.evolutionnews.org/2010/11/douglas_theobald_tests_univers041021.html Douglas Theobald's Test Of Common Ancestry Ignores Common Design – December 1, 2010 http://www.evolutionnews.org/2010/12/douglas_theobalds_test_of_comm041071.html But Isn't There a Consilience of Data That Corroborates Common Descent? - Casey Luskin - December 2, 2010 Excerpt: Dr. Theobald might have had a point, were it not for the fact that: (1) Phylogeny and biogeography don't always agree. (2) Phylogeny and paleontology don't always agree. (3) Transitional fossils are often missing (or the "predicted" transitional fossils fall apart on closer inspection). (4) Hierarchical classifications often fail. (5) "Homologous" structures often have different developmental pathways or different structures often have "homologous" developmental pathways. (6) Morphological and molecular phylogenies are often incongruent. http://www.evolutionnews.org/2010/12/but_isnt_there_lots_of_other_d041111.html

It is sad testimony to the 'science' of Darwinian evolution that they have to rely on fraudulently derived 'statistical significance' in order to try to provide any supposed proof for their theory. Verse:

1 Thessalonians 5:21 but test everything; hold fast what is good.

bornagain77

February 18, 2017

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