Warning: I might be forgetting basic statistics here. Please edit title if it can be improved.

This paper, seemingly summarized in the fancy ZUI powerpoint here, points out a possible "critique" in its... methodology or hypothesis choice or something (slides 26-27)?

The paper creates a regression with credit score (FICO) against some variables. Here is the model (It strangely doesn't have a $\beta_0$):

$FICO = -28.56(OtE) + 45.97(C) - 11.79(E) - 35.12(A) + 8.61(N) + 0.003(TP) + 0.002(OCBO) + 0.002(OCBI) + 0(PD) + 0(PA)$

The paper has a hypothesis for each variable being positively or negatively correlated with credit score (FICO), except for OtE, which was not expected to have any link (it says "no hypothesis", but I suspect this is layman for hypothesis of no statistical significance).

1. The correlation between OtE and credit score (FICO) is -0.17.

2. The $\beta$ of OtE is -28.56.

3. The correlation is deemed significant with $p < 0.05$.

So what exactly is the critique here?

It's a significant (despite having no expectation of any significance, whether positive or negative) negative correlation, but it's not a high negative correlation?

Maybe they meant that it is a critique because it is their only hypothesis that is significantly not true (All the others are either significantly true or not significantly true, I think) ?

If not...

I recall testing for statistical significance is testing $\beta = 0$. Is the correlation being significant equivalent to statistical significance ($\beta \neq 0$) ?

It seems like they are instead saying that $\beta = 0$ is false from the calculated -28.56. I don't recall being able to conclude significance of a coefficient from its number. 28.56 may be far from 0 relative to 6 or 0.01, but it may be near relative to 90 or 1,000,000.

Warning: I might be forgetting basic statistics here. Please edit title if it can be improved.

This paper, seemingly summarized in the fancy ZUI slideshow here, points out a possible "critique" in its... methodology or hypothesis choice or something (slides 26-27)?

The paper creates a regression with credit score (FICO) against some variables. Here is the model (It strangely doesn't have a $\beta_0$):

$FICO = -28.56(OtE) + 45.97(C) - 11.79(E) - 35.12(A) + 8.61(N) + 0.003(TP) + 0.002(OCBO) + 0.002(OCBI) + 0(PD) + 0(PA)$

The paper has a hypothesis for each variable being positively or negatively correlated with credit score (FICO), except for OtE, which was not expected to have any link (it says "no hypothesis", but I suspect this is layman for hypothesis of no statistical significance).

1. The correlation between OtE and credit score (FICO) is -0.17.

2. The $\beta$ of OtE is -28.56.

3. The correlation is deemed significant with $p < 0.05$.

So what exactly is the critique here?

It's a significant (despite having no expectation of any significance, whether positive or negative) negative correlation, but it's not a high negative correlation?

Maybe they meant that it is a critique because it is their only hypothesis that is significantly not true (All the others are either significantly true or not significantly true, I think) ?

If not...

I recall testing for statistical significance is testing $\beta = 0$. Is the correlation being significant equivalent to statistical significance ($\beta \neq 0$) ?

It seems like they are instead saying that $\beta = 0$ is false from the calculated -28.56. I don't recall being able to conclude significance of a coefficient from its number. 28.56 may be far from 0 relative to 6 or 0.01, but it may be near relative to 90 or 1,000,000.