In statistics, there are some fundamental principles:
Type I errors, Type II errors
Null hypothesis



Null hypoth true Null hypoth false

Reject Null hypothesis Type I Error Correct

Fail to reject null Correct Type II error


What is the VC'’s null hypothesis upon reading a business plan? This is a bad investment, and pass on it.

So what happens when the null hypothesis is true and we reject? We did the right thing.

What happens when the null hypothesis is false (and this is a good investment)
Then we can have a type II error.

I'’m paid to avoid type I errors. Ones where we make an investment into a bad company

I can make type II errors (missing out on the yahoos, googles, intels, microsofts) and not get roasted too much.

If I make a type I error, and put money into a company that'’s not a good company, then I should polish up my resume.

The type I error is horrible.
The type II error is acceptable.

Type I can be seen as a false alarm, and seeing a good investment where there is none.
Type II error is missing a real effect, not recognizing a good company.

Given that the smaller the sample the more likely you’ll commit a type II error, a type II is almost unavoidable, given the small number of bplans that are out there.

Here'’s the big thing
The null hypothesis is that the company is a bad investment.
We presume that the company is a bad investment from the start.

Then we try to find reasons it’s a good company.
Then reasons to reject.
Until we're confident that an investment is the right thing to do. Otherwise, go with the null hypothesis, presume it'’s a bad investment, and move on with your life.

So when you get a rejection letter. Understand we’re more likely to accept the Type II error, and your company can fall into that category, without an investor feeling bad about the result, or feeling bad about miscategorizing you.