My approach in these matters is Bayesian: Do having female, black, Asian, Indian, etc. executives drive start-up success? The a priori probability I assign to this statement is 0.5, i.e. may or may not. I also employ the My Human Law of Large Numbers, i.e. any "large enough" human population (i) has a Gaussian distribution of any cognitive skill and (ii) the parameters of this distribution is pretty much independent of the particular population sample. I don't have solid proof of this principle and in certain subdomains it may be wrong (e.g. the great cognitive differences between men and women debate, etc.) but I doubt that population differences would be significant.
Now, armed with the simple Bayesian approach and the MHLLN, we can see that most of these articles are BS. The evidence to move the a priori value of 0.5 up or down should be substantial, e.g "extraordinary claims require extraordinary proof". I would be extremely surprised if any gender, racial, etc. factor would derive success of any size company.
Since the above analysis is rather trivial, one then has to ask why these things continue to be written. I think the motivation is usually benign: One sees the dearth of women in startups and wants to show that "it's a good thing". This approach, however, is misguided in that, by making silly arguments or sub-par statistical analysis, it hurts the cause due to the "the lady doth protest too much" effect: many people politely nod, but see through your sloppiness and internally become convinced of just the opposite cause (especially if they are inclined to do so, i.e. if the prior was less than 0.5).
A quote I like a lot is "To be ideological is to preconceive reality." These authors, rather than being objective, have already decided what their results will be and are just filling up the blanks.