Nah let's test our kids once more , just to be sure we can survive.

Well that is just because it is not well designed.

Let me get it straight for you.

The p-value expresses how likely a value is regarding the null hypothesis ( everything is random )

The pearson tell you how correlated two variables are.

His weird indicator divides the pearson by the p value which is ... not good. His value indicate that there is a high chance there is an effect in some cases but the pearson indicates the effect is minimal.

But we lack significance of data.

A meh but usable pearson is over 0.70, a good p value is under 0.1.

He barely got both for the definition of one category. Meaning there is one category where his predictor works sorta well and can be considered not random.

This is due to bivariate analysis on an obviously more complex problem.

He should include other indicator and try to show a correlation ( a multivariate analysis )

He also should try to do some PCA to understand better where the variance come from.

I am not convinced . Theses results are low, also you should provide more data such as variance and average.

It's lacking in drawing any significant statistic.

And if you are trying to prove the actual lack of correlation, the amount is still insufficient.