This is the example I am looking at.
Question:
Suppose a family has $2$ children and one is a boy, and that the probability of having a child of either sex is equal and independent across children. What is the probability that they have $2$ boys?
Now going by intuition, I would say that the sample space would be this. $$\{B, B\}, \{B, G \}, \{G , B\} \text{ and }\{G , G\}.$$
So given that the first child is a boy. Then there is a $50\%$ $(1/2)$ chance for the second child to also be a boy.
But going by the Bayes' rule the problem is solved like this.
Let $\mathscr S = \left\{\{B,B\},\{B,G\},\{G,B\},\{G,B\}\right\}.$ Then
$$ \Pr(\{B, B\} \mid |B ≥ 1)=\frac{\Pr(B ≥ 1| \{B, B\})\times \Pr(\{B, B\})}{\sum\limits_{S\in\mathscr S}\Pr(B\geq 1\mid S)\Pr(S)}.$$ Therefore after reduction: $$\Pr(\{B, B\} |B ≥ 1)= 1/3.$$
The link to how this was solved is here.
I understand that when dependent events are in play we need to use the Bayes rule. When I apply the formula I get $1/3$ too. What I don't understand is what part of my fundamental understanding that is incorrect. Why is $1/2$ incorrect. When to identify and avoid such wrong intuitions.
Thanks for your time.