Tchebysheff proof, help understanding transition to last step

In summary: The proof has assumed that p is true, which is why they can conclude that (p OR q) is true, no matter what q is! q doesn't ever have to be true since we've assumed p is. This is called (addition or generalization), and it's a fact in logic.
  • #1
el_llavero
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proof attached as pdf in link provided
 

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  • #2
Using A for |A| and kk for k squared:

1 > Akk/n
1 < n/(Akk)
A < n/kk
A/n < 1/kk
1 - A/n > 1 - 1/kk

So the stated result actually holds with strict equality.
 
  • #3
I actually got some help with the algebraic manipulation, which was clouding my conceptual understanding, after looking at this more I realized what it all meant and why the use of >= instead of > at the end


Divide by k^2 results in 1/(k^2)>|A|/n

multiply both sides by -1 (fips inequality) and add 1 to both sides results in 1- 1/k^2 < 1-|A|/n

change from strict inequality to weak inequality to account for proportion of all elements "~A" INTERSECTION "B" results in
1- 1/k^2 <= 1-|A|/n
 
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  • #4
EnumaElish said:
Using A for |A| and kk for k squared:

1 > Akk/n
1 < n/(Akk)
A < n/kk
A/n < 1/kk
1 - A/n > 1 - 1/kk

So the stated result actually holds with strict equality.

But the only way 1 - A/n > 1 - 1/kk describes the proportion of measurements in B, i.e. ~A, is to switch to weak inequalitysince strict inquality leaves out part of B, t/f weak inequality is required.

What do you think?
 
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  • #5
|A| is a real number, like 2.

If 2/n < 1/kk then 1 - 2/n > 1 - 1/kk. Not so?
 
  • #6
EnumaElish said:
|A| is a real number, like 2.

If 2/n < 1/kk then 1 - 2/n > 1 - 1/kk. Not so?

the cardinality of A, i.e. |A|, is a natural number, an integer in the set {0,1,2,3,...}

if |A|=1, and k =1 and n = 1 then the equations are equal. that's assuming that there is no restriction on n being equal to 1 and card(A) being equal to 1.
 
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  • #7
Of course, if the premise were 2/n < 1/kk then 1 - 2/n > 1 - 1/kk would be the case. But the premise is stated with strict inequality.
 
  • #8
yes you're right. So the use of weak inequality may have something to do with describing the complement of A?
 
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  • #9
Or what they meant was 2/(n-1) < 1/kk, then they replaced n-1 with n but forgot to change < to <.
 
  • #10
Perhaps that's the case, because the strict inequality was used to derive the last line. Moreover, perhaps my set element argument was more of a way to try and rationalize the use of the weak inequality. I'm going to try and get a response from the author of this proof, and see what he says. Thanks for your responses EnumaElish.
 
  • #11
I corresponded with a professor from a previous class about this and he reminded me about the use of logic in proofs

If you assume p is true then you may also conclude that ( p OR q) is true, no matter what q is! q doesn't ever have to be true since we've assumed p is. It's a fact in logic referred to as (addition or generalization).

p is LHS > RHS, and q is LHS = RHS.
 
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  • #12
I am not saying the proof is wrong; I am saying it can be more definitive.
 
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FAQ: Tchebysheff proof, help understanding transition to last step

What is the Tchebysheff proof?

The Tchebysheff proof is a mathematical proof technique that is used to prove inequalities. It is based on the Tchebysheff inequality, which states that the probability that a random variable will deviate from its mean by more than a certain amount is less than or equal to the variance of the random variable divided by that amount squared.

How does the Tchebysheff proof work?

The Tchebysheff proof works by using the Tchebysheff inequality to bound the probability of a random variable deviating from its mean. This bound is then used to prove an inequality involving the random variable and its mean. The proof typically involves a series of algebraic manipulations and logical deductions.

Why is the Tchebysheff proof useful?

The Tchebysheff proof is useful because it allows us to prove inequalities involving random variables without having to know the exact probability distribution of the random variable. This makes it a powerful tool in statistics and probability theory, as many real-world problems involve random variables with unknown distributions.

What is the last step in the Tchebysheff proof?

The last step in the Tchebysheff proof is typically to use the bound obtained from the Tchebysheff inequality to prove an inequality involving the random variable and its mean. This is often done by setting the bound equal to the desired inequality and then solving for the desired variable.

How can I better understand the transition to the last step in the Tchebysheff proof?

To better understand the transition to the last step in the Tchebysheff proof, it is important to have a solid understanding of the Tchebysheff inequality and how it is used to bound the probability of a random variable deviating from its mean. It is also helpful to have a strong background in algebra and logical reasoning, as these skills are often utilized in the last step of the proof.

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