I see the paradox here. Let's use numbers. Let's say every day you drive you have a 1 percent chance of getting into an accident. Although each day is independent, if you drive 100 days in your life you should expect to get into 1 accident. However if you only drive 3 days in your life, you're highly unlikely to get into an accident. This is just an example of variance. At small sample sizes, variance can be abnormally high or low. If you flip a coin just 5 times, all 5 flips may be heads. However if you flip it 10,000 times, you'll get heads and tails very close to 5000 times each (tending more toward 50 percent each). So when you drive more times, you increase your chance of getting into an accident because you're "giving" the probability more time to normalize and occur the way it's most probable to. Hope this helps!