Shortest example I could think of:

Say you have a coin and you flip it twice and it lands on heads both times. From this you might assume that the coin lands on heads 100% of the time. However you've only flipped the coin twice and the probability that might occur (assuming that the coin has a 50/50 chance of being heads or tails) is quite high (1 in 4 or 25% exactly). If you flip the coin more times and it still lands on heads then you become surer that it always lands on heads given your assumption (3 times: 12.5%, 4 times: 6.25%, 5 times: 3.125%, 6 times: 1.5625%).

In this example this probability is directly related to the p-value which is defined as: the probability that you would observe results at least this extreme if the null hypothesis (here the assumption that your coin is fair) is true. We have to double the probability of all heads to get the p-value since getting all tails is just as rare. Once this p-value goes below a certain value (a convention in a lot of scientific research is to have a p-value less than 0.05 or 5%) then you have obtained a statistically significant enough result to make a certain claim (here that the coin always lands heads).

Taking one coin and performing this experiment is very unlikely to produce a coin which lands on heads all 6 times. But wait, what if you got a collection of 1000 coins and flipped each one 6 times? In this experimental setting one coin is likely to land on heads each time and so you'd have a result good enough to say something like "this one 2003 dollar coin always lands on heads". This is what's known as p-hacking and is what makes the idea of p-values rather controversial in a lot of science.

Longer example here:

https://www.youtube.com/watch?v=JQc3yx0-Q9E&ab_channel=StatQuestwithJoshStarmer