Assumption: There is no fundamental difference between a female and a male founder for achieving start-up success (average rates and variance/distribution of rates is the same)
Observation: VC funded start-ups with female founders are (on average) 60% more successful than start-ups with male founders
Hypothesis: VC funding is biased against female founders. The ones that do receive funding are better vetted, less risky, and have higher individual qualities.
Experiment: Start funding more female founders.
If we then observe: The numbers start to even out, then there is no fundamental difference. VC funding bias may have been the cause of the difference in success rate.
If we then observe: The numbers stay the same, then there is a fundamental difference and our assumption is flawed.
Rational choice: Start funding more female founders. This either removes a bias (levels the playing field), or increases your profit (funding more potentially successful founders).
PG should of course not use an hypothesis to prove an assumption (experiment/probing is needed for verification). But also: The possibility of an uneven distribution should not invalidate such an experiment (or PG's line of reasoning), it will merely bring it to light (the numbers would stay the same, thus we have shown that the difference is fundamental and not caused by a sampling bias).
Assumption: There is no fundamental difference between a female and a male founder for achieving start-up success (average rates and variance/distribution of rates is the same)
Observation: VC funded start-ups with female founders are (on average) 60% more successful than start-ups with male founders
Hypothesis: VC funding is biased against female founders. The ones that do receive funding are better vetted, less risky, and have higher individual qualities.
Experiment: Start funding more female founders.
If we then observe: The numbers start to even out, then there is no fundamental difference. VC funding bias may have been the cause of the difference in success rate.
If we then observe: The numbers stay the same, then there is a fundamental difference and our assumption is flawed.
Rational choice: Start funding more female founders. This either removes a bias (levels the playing field), or increases your profit (funding more potentially successful founders).
PG should of course not use an hypothesis to prove an assumption (experiment/probing is needed for verification). But also: The possibility of an uneven distribution should not invalidate such an experiment (or PG's line of reasoning), it will merely bring it to light (the numbers would stay the same, thus we have shown that the difference is fundamental and not caused by a sampling bias).