Successive Replacement
Imagine you have a bag of 10 marbles — 4 red and 6 blue. You draw one marble, note its colour, and put it back. Then you draw another. The chance that both draws are red is 104×104=0.16. That's straightforward because the bag is exactly the same for the second draw.
Now change the game: you draw a marble and do not put it back. The bag now has only 9 marbles. If the first was red, only 3 reds remain; if it was blue, all 4 reds are still there. The probability of the second event depends on what happened first. This is the core of successive replacement — or rather, its absence.
Successive replacement is the process of drawing items one after another from a finite set, returning each item to the set before the next draw. The key consequence: the composition of the set never changes. Every draw is identical and independent of every other draw.
The Precise Statement
Let a set contain N items, of which K are of a particular type (say "success"). You draw n items one at a time, replacing each item before the next draw. Then:
- The probability of getting a success on any single draw is always NK.
- The draws are independent — the outcome of one draw does not affect the next.
- The number of successes in n draws follows a binomial distribution:
P(exactly r successes)=(rn)(NK)r(1−NK)n−r
P(r successes in n draws with replacement)=(rn)pr(1−p)n−r,p=NK
Why It Matters
Without replacement, probabilities shift after every draw — that's the hypergeometric distribution, and it's messier. With replacement, you get the clean, constant-probability binomial model. This is why successive replacement is the default assumption in many exam problems unless "without replacement" is explicitly stated. …