Who Owns the Standard Definition of Evolution

Although it didn't fool many people, when he began he was trying to appear like someone making an innocent inquiry about evolution, so he avoided the word "kind" and somehow fixed upon the term "lifeform." It's not wrong, I guess, but I agree that it's weird. But it's a familiar schtick, right? It doesn't matter what we say, they just keep circling back. Oh, I didn't pick up on that. I think you're right, that he thinks macroevolution is some additional process that evolutionary biologists claim happened even though they have no evidence of it and have no idea how it could have happened. Have we said that macroevolution is just a lot of microevolution yet? Yep! Well put. He seems to think that scientists are egotists who like making pronouncements about things they know nothing about.--Percy

Let's start here: What we are ultimately asking is how confident can we be that your coin is fair given this one trial.We would first ask how probable it would be for a fair coin to produce 10 tails in 10 tosses. One mistake I made earlier to is ignore the other tail on the probability graph, so in this instance we have to ask how probable it is to get this extreme result. This would necessarily include 10 heads or 10 tails. The probability of getting 10 heads or 10 tails in 10 tosses is 2 in 1024, or 0.001953125. Therefore, we can say that your coin is unfair with a p value of 0.001953125.We could even ask what results we would need to see for a fair coin flip with 95% confidence (i.e. p<0.05). We would only need to calculate 2.5% on either side of the probability distribution since we know the distribution should be normal.where:
n=number of trials
r = number of tails
p=probability of a fair flipSince the first part is the formula for combinations (nCr) I broke it down like this in Python for calculating all results based on number of tails:
[note: python interpreter for those who want to run the code, just paste in all code blocks to Online Python Compiler (Interpreter) ]

import math

def coin_combo(n, r):
    ncr = math.factorial(n)/(math.factorial(r)*(math.factorial(n-r)))
    return(ncr)

num_trials = 10
tot_combos = 0
for i in range(0, num_trials+1):
    combos = coin_combo(num_trials, i)
    tot_combos = tot_combos + combos
    print(f"{i} tails in {num_trials} trials has {combos} combinations")

print(f'\nThere are {tot_combos} total combinations')
print(f'5% confidence interval begins at {0.025*tot_combos}')

output:

0 tails in 10 trials has 1.0 combinations
1 tails in 10 trials has 10.0 combinations
2 tails in 10 trials has 45.0 combinations
3 tails in 10 trials has 120.0 combinations
4 tails in 10 trials has 210.0 combinations
5 tails in 10 trials has 252.0 combinations
6 tails in 10 trials has 210.0 combinations
7 tails in 10 trials has 120.0 combinations
8 tails in 10 trials has 45.0 combinations
9 tails in 10 trials has 10.0 combinations
10 tails in 10 trials has 1.0 combinations

There are 1024.0 total combinations
5% confidence interval begins at 25.6

Our 5% cutoff begins at 25.6 combinations at either end of the probability distribution. We would have to sum the probabilities for each result, so for 0 tails we have 1, 1 tail we have 10, and for 2 tails we have 45. 56 is over the 25.6 combinations and 11 is below it. Therefore, our cutoff is 1 tail or 9 tails. If you have a result with 3 to 8 tails then we have 95% confidence that your coin is fair, but with 0, 1, 9, or 10 tails we can say that your coin flip is unfair with p<0.05. This is also backed up by the Central Limit Theory at 2 sigmas which is close to 95% confidence:std^2 = np(1-p)where:
std=1 standard deviation
n=number of trials
p=probability of a fair toss (i.e. 0.5)Also, see here:
Is my coin fair ?. Notes on statistical hypothesis… | by Paros Kwan | MediumIn python:

num_trials = 10
p= 0.5
sigma = math.sqrt(num_trials*0.5*(1-0.5))

print(num_trials*p-sigma*2)
print(num_trials*p+sigma*2)

output

1.8377223398316205
8.16227766016838

Again, what we got before. So it checks out from two different statistical tests. BTW:"Assuming the null hypothesis to be true, if the experimental result lies within the most extreme 5% of the distribution, in statistic, we would reject the null hypothesis and the alternative is in favour ,as it is very unlikely that we can just do a random experiment, and hit such an extreme result. The threshold 5%, is called the significant level (α), and it is a generate practices to set it to 5% or 1%. The probability(p) of an experimental outcome, given that null hypothesis hold, is called the p-value, if p < α, we would reject null hypothesis."
Is my coin fair ?. Notes on statistical hypothesis… | by Paros Kwan | MediumThat's exactly what I said before. You claim this is wrong, but it is found throughout all of the equations and websites describing these statistical tests.

PaulK:

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In Message 153 Taq’s actual claim is:
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Taq:

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The often used p value in science refers to the chances that a random set of data will produce a false positive.

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You really are a lying and deceitful bastard, aren't you?Here is my comment and Taq's reply without you censoring:

sensei:

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You clearly don't understand the difference between the odds of an observation and the odds or level of doubt for a theory being true or not.

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Taq:

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Those are one in the same. The often used p value in science refers to the chances that a random set of data will produce a false positive.

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He did claim they are the same. So move on now as I have no business in lying evotards.

If that is what you got from it then you failed to understand even the basics of the evidence. It doesn't discuss common materials. It discusses a nested hierarchy:

quote:

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Mere similarity between organisms is not enough to support macroevolution; the nested classification pattern produced by a branching evolutionary process, such as common descent, is much more specific than simple similarity. Real world examples that cannot be objectively classified in nested hierarchies are the elementary particles (which are described by quantum chromodynamics), the elements (whose organization is described by quantum mechanics and illustrated by the periodic table), the planets in our Solar System, books in a library, or specially designed objects like buildings, furniture, cars, etc.
29+ Evidences for Macroevolution: Part 1

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You have yet to demonstrate why a Creator would necessarily produce such a pattern of both similarities and differences. Just blurting out "is as much or more of an argument for a Creator" just doesn't cut it. You need to explain WHY a nested hierarchy is a necessary outcome from separately created kinds. They aren't completely different. Both reptiles and mammals are amniotes. They are the same kind of lifeform. Humans are amniotes. Reptiles are amniotes. Our common ancestor was an amniote. It is amniotes evolving into different species of amniotes.Again, you are ignoring the nested hierarchy. That isn't the explanation. The evidence is the nested hierarchy which is a pattern of shared features.

Nope. The website below says the very same thing:

quote:

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Assuming the null hypothesis to be true, if the experimental result lies within the most extreme 5% of the distribution, in statistic, we would reject the null hypothesis and the alternative is in favour ,as it is very unlikely that we can just do a random experiment, and hit such an extreme result. The threshold 5%, is called the significant level (α), and it is a generate practices to set it to 5% or 1%. The probability(p) of an experimental outcome, given that null hypothesis hold, is called the p-value, if p < α, we would reject null hypothesis.
Is my coin fair ?. Notes on statistical hypothesis… | by Paros Kwan | Medium

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p values work exactly as I described.

Taq:

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That's exactly what I said before. You claim this is wrong, but it is found throughout all of the equations and websites describing these statistical tests.

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Here is what I wrote to Percy, in case you missed it:

sensei:

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Taq claims that it is the same as the p-value, which is simply not true. The p-value of 1/1024 gives the probability of observing ten times tails, given that we have a fair coin. If you know any Baysian statistics, you should know that this is not the same as the probability that we have a fair coin, given what we observed. Prob(A|B) ≠ Prob(B|A). Very rooky mistake by Taq.

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I suggest you read this carefully, study it, do excercises on it and then maybe you actually learn, what you should have learned long before you started working in science.

Tangle:

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In normal discussions with normal human beings when that kind of statement is made it's normally followed by an explanation of the error being made.

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You made the same mistake as Taq so maybe also read the above, I suppose.

That's not the notion. The notion is the observation of an objective nested hierarchy, not simply similarities. The theory of evolution predicts that this pattern should be seen, and it has been empirically verified.

Read it and weep:

quote:

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Assuming the null hypothesis to be true, if the experimental result lies within the most extreme 5% of the distribution, in statistic, we would reject the null hypothesis and the alternative is in favour ,as it is very unlikely that we can just do a random experiment, and hit such an extreme result. The threshold 5%, is called the significant level (α), and it is a generate practices to set it to 5% or 1%. The probability(p) of an experimental outcome, given that null hypothesis hold, is called the p-value, if p < α, we would reject null hypothesis.
Is my coin fair ?. Notes on statistical hypothesis… | by Paros Kwan | Medium

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That fully backs up all of my descriptions and math. Read the entire webpage. Perhaps you will learn something.

Taq:

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Assuming the null hypothesis to be true, ...

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From your website, Taq, you miss the key start of the sentence, "assuming the null hypothesis to be true ...",The p-value relates to the probability of the observation, after assuming the hypothesis. That is not the same as the probability of the null hypothesis being actually true.When do you finally learn, dummy?

Here are 29+ empirically demonstrable conclusions:29+ Evidences for Macroevolution: The Scientific Case for Common DescentEvery section has a prediction, potential falsification, observations, and conclusion.

Taq:

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That fully backs up all of my descriptions and math.

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Okay sure, it has been confirmed yet again that you are a low intellect, incapable of understanding even when it's spelled out for you. Have a nice day.

Taq:

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Here are 29+ empirically demonstrable conclusions:

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Taq, you are in no position to make any scientific claims what so ever with your level of understanding.

Exactly. It assigns a confidence level to the conclusion just as in the case of the match between independent phylogenies, which was the original topic. I even linked the p value calculator so you could find the p value for yourself. For two rooted independent trees with 30 taxa the p value for the match is p< .0194686103660492e-39.29+ Evidences for Macroevolution: Statistics of Incongruent Phylogenetic Trees

Then go to the website and show how those scientific claims are not empirically demonstrable conclusions. If you think you are such a smarty pants, then address the conclusions made at TalkOrigins and show how they aren't scientifically supported.

Taq:

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It assigns a confidence level to the conclusion just as in the case of the match between independent phylogenies, ...

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Don't tell me that you also don't know the difference between confidence levels and p-values. You talk as if they are the same. LOL. I'm laughing hard in your face!They are not the same at all. And if you knew that already, then you should know that your comment makes no sense and is bullshit!Really, the more you comment, the more you prove yourself to be incapable.