This sounds like a simple question, but here's the gist:
Given a coin flip (or some other random process that can result in one of two outcomes) that has a perfect 50-50 probability of landing on heads or tails (the probability of heads is 50%, the probability of tails is 50%), if I were to flip the coin 10 times, the results would be close to 5-5. If I flip it 100 times, the results would be close to 50-50. The larger my sample size, the closer the results reflect the probability.
But if I flip this coin once, there's a 50-50 chance of landing on either heads or tails. The next time I flip the coin, the probability is the same. This means that each result of, say, 20 flips would be equally likely (8 heads and 12 tails and 10 heads and 10 tails would be equally likely).
If this is true, why do the results of flipping a coin many times trend towards an equal split? If this isn't true, why not?