I would like to understand the difference between the $\chi^{2}$ distribution and the Probability-To-Exceed ?

I have to compare 2 data sets A and B and in the article I am reading, they talk about this PTE :

enter image description here

I only know the $\chi^{2}$ distribution with $k=2$ degrees of freedom :


and the relation with confidence level :


I don't know how to do the link with the text above.

In the article, they make appear the integral of gaussian whereas in $(2)$, I can only make appear a simple integration of exponential (I mean, there is no "$\text{erf}$" function appearing unlike into the article).

If someone could tell me the difference between $\chi^{2}$ distribution and $P_{\chi^2}$ (PTE) ?

UPDATE 1: the context is about astrophysics where I have to compare the consistency of 2 data sets (cosmological parameters) . The method is described below :

enter image description here

enter image description here

Could anyone tell me what's the definition of this Probability-To-Exceed and how to determine it ?

Is it a cumulative function ? How to get the integral of a gaussian in this case (since erf appears) ?

Any help is welcome, regards

  • $\begingroup$ I find this quotation so unintelligible that I suppose a good answer might require referring to the original context. Please, then, tell us the source. $\endgroup$
    – whuber
    Commented Jan 3, 2019 at 21:35
  • $\begingroup$ @whuber The context is about an astrophysical context where I have to compare the consistency of 2 data sets. You can see more in my UPDATE 1 that describes the method $\endgroup$
    – user226073
    Commented Jan 3, 2019 at 23:18
  • 2
    $\begingroup$ In the citation, "the expected variance of random variable". The variance is expectation of something, so we have no expected variance. $C_A$ and $C_B$ are matrices, but $C_A + C_B$ is the variance. I think you may need to find other books/papers. $\endgroup$
    – user158565
    Commented Jan 4, 2019 at 2:29
  • $\begingroup$ @user158565 I think that $C_{tot}$ is the sum of the two covariance matrices $C_{A}$ and $C_{B}$, you don't need to focus of "the expected variance". But what it matters is to know the general method to test independant or non-correlated shared parameters between the 2 data sets with independant expriments $A$ and $B$ : I would like to grasp the subtilities of the method, especially by the formula of $\chi^{2} = (p_{A}-p_{B})^T\,C_{tot}^{-1}\,(p_{A}-p_{B})$ : how can I prove this relation ? $\endgroup$
    – user226073
    Commented Jan 5, 2019 at 23:22
  • $\begingroup$ Moreover, what does mean "the treshold for evidence of tension" for $3\sigma$ and "definitive evidence of tension" for $5\sigma$ ? It would mean that if difference between 2 data sets is greater than $5\sigma$, so it would break all previous estimations, wouldn't it ? But how to compute the "difference between 2 data sets" ? regards $\endgroup$
    – user226073
    Commented Jan 5, 2019 at 23:26

1 Answer 1


Instead of asking what the difference is between the two, it's clearer to ask what the relationship is between πœ’2 distribution and the Probability-To-Exceed (PTE).

The PTE is the probability of obtaining a higher πœ’2 than what you actually achieved. πœ’2 is a measure of how far off your values are from expectation, and a higher value means larger disagreement. A very low PTE means it is very unlikely to get a higher πœ’2 than what you already have, meaning your values are farther off from expectation than random chance would allow. In the opposite extreme, a very high PTE means it is very likely to get a higher πœ’2; this is also bad because it usually means you have overestimated the errors on your measurement.

To calculate the PTE, integrate the πœ’2 distribution up to your value of πœ’2, and subtract that value from 1. Usually this is done via a look-up table or solved numerically with a computer program, since there is not a closed-form solution.

The quoted text then goes further, wanted to relate this PTE into a "sigma" of a gaussian distribution since that is a more commonly understood metric.


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