Independence intuition Toss two fair dice. There are $36$ outcomes in the sample space $\Omega$, each with probability $\frac{1}{36}$. Let:


*

*$A$ be the event '$4$ on first die'.

*$B$ be the event 'sum of numbers is $7$'.

*$C$ be the event 'sum of numbers is $8$'.


It says here $A$ and $B$ are independent. I don't understand why this is the case. What is the intuition behind this? Can someone offer an explanation to me that doesn't involve using the definition of $\mathbb{P}(A \cap B) = \mathbb{P}(A)\mathbb{P}(B)$?
My understanding is informally, an event is independent if the occurrence of one does not affect the probability of the other and vise versa. So if $A$ occurs, wouldn't that affect the probability of $B$? Since if I were to roll a $4$ on the first die, the sample space will be reduced and hence the probability of 'sum of numbers is $7$' will be affected?
It also says $A$ and $C$ are not independent and $B$ and $C$ are not independent. Why?
I think this is because I'm confusing independence with conditional probability?
 A: Since we are using fair dice, the atomic outcomes have equal probability measure. 
$\begin{array}{l}
A = \{(4,1), (4,2), (4,3), (4,4), (4,5), (4, 6)\}
\\
B = \{(1,6), (2,5), (3,4), \color{blue}{(4,3)}, (5,2), (6,1)\}, & A\cap B=\{(4,3)\}
\\
C = \{(2,6), (3,5), \color{blue}{(4,4)}, (5,3), (6,2)\}, & A\cap C = \{(4,4)\}
\end{array}
\\[2ex]\;\\
\begin{array}{l}
 \mathsf P( A) = 1/6, &\mathsf P(B) = 1/6, &\mathsf P(A\cap B)=1/36, &\mathsf P(A\mid B) = 1/6 , &\mathsf P(B\mid A) = 1/6
\\  &\mathsf P(C) = 5/36, &\mathsf P(A\cap C)=1/36 , &\mathsf P(A\mid C) = 1/5, &\mathsf P(C\mid A) = 1/6
\end{array}
$
The independence of $A$ and $B$ is because the proportion of outcomes for $A$ that occur within the space of $B$ is the same as the proportion of outcomes for $A$ within the total space.   Likewise the proportion of outcomes for $B$ that occur within the space of $A$ is the same as the proportion of outcomes for $B$ within the total space. 
However, this is not so for the proportion of outcomes for $A$ which occur within the space of $C$.
NB: if the dice we biased we would have to consider the weight of each atomic outcome, rather than simply counting.
A: How do you think '$4$ on the first die' affects the chances of getting a total of $7$? Do you think it increases it, or decreases it? Let's do the math.
$6$ on the first die: we need $1$ on the second die. One chance in $6$.
$5$ on the first die: we need $2$ on the second die. One chance in $6$.
$4$ on the first die: we need $3$ on the second die. One chance in $6$.
$3$ on the first die: we need $4$ on the second die. One chance in $6$.
$2$ on the first die: we need $5$ on the second die. One chance in $6$.
$1$ on the first die: we need $6$ on the second die. One chance in $6$.  
So the chance of getting a total of $7$ is the same regardless of what comes up on the first die. Knowing the first die came up $4$ does not make the chances of $7$ better or worse. Events $A$ and $B$ and independent.
Now what if we want a total of $8$? How is that different?
$6$ on the first die: we need $2$ on the second die. One chance in $6$.
$5$ on the first die: we need $3$ on the second die. One chance in $6$.
$4$ on the first die: we need $4$ on the second die. One chance in $6$.
$3$ on the first die: we need $5$ on the second die. One chance in $6$.
$2$ on the first die: we need $6$ on the second die. One chance in $6$.
$1$ on the first die: we need $7$ on the second die. ZERO CHANCE.
So '$1$ on the first die' lowers the chances of 'total of $8$' (to zero); anything else raises the chances to $1/6$. In particular, events $A$ and $C$ are dependent. The unconditional (i.e. before we know the result for the first die) probability of 'total of $8$' is the average of $\{1/6,\ 1/6,\ 1/6,\ 1/6,\ 1/6,\ 0\}$ which is $5/36.$
Looking at it the other way round, given that the total is $8$, we know that we can't have $1$ on the first die, so the conditional probability of $A$ given $C$ is not $1/6$ but $1/5$.
A: Here's an analogous example from my previous post:
Pairwise independence can be characterised as:

Let $P(A)\neq0.$
Events $A$ and $B$ are independent iff. the knowledge that $A$ happens doesn't change $B$'s probability.

When considering whether events $X$ and $Y$ are independent, the correct intuition is to ask whether the knowledge that $X$ happens changes the probability of $Y$—not whether it restricts how $Y$ can happen.
Consider this experiment: flip two fair coins, letting $H_1$ be the
event that the first coin lands on Heads, and $X$ be the event that
the coins land on different sides. Then $$ P\left(H_1 \cap
X\right)=\frac14=P(H_1)\:P(X);$$ i.e., Events $H_1$ and $X$ are, by definition, independent. Despite this, the knowledge that $H_1$ happens has reduced the number of ways that $X$ can happen: one of $X$'s outcomes, $TH,$ is no longer possible.
A: $A$ effects the probability of $B$... and leaves it untouched. Getting 4 on the first die excludes many cases of $A$ and $A^c$, but does so evenly, so $P(B|A)=1/6=6/36=P(B)$.
