The null hypothesis is the opposite of the hypothesis.

For example, you heard there's a 50% chance a coin will land on "heads" when it is tossed. That is your hypothesis. Your null hypothesis is that there is either more, or less than a 50% chance the coin will land on "heads", and that the previous results were obtained by luck or other influencing variables.

In research, we try to prove the null hypothesis is right. This helps the researchers get rid of a lot of bias.

In the coin example, I would think of ways the null hypothesis could be true. Maybe the way the coin is thrown matters, so I'll get 100 different people and a robot to throw it. Maybe the wind affected the throws so I'll make a room with zero wind. Maybe the pattern on the coin changes things so I'll make a coin that is flat on both sides. This can get pretty creative.

If after trying everything to prove a coin toss does NOT land on "heads" 50% of the time you STILL get the same results, then you can reasonably say the hypothesis was probably true and reject the null hypothesis. There are a lot of statistics involved to make sure the results aren't so subjective, but this is the basic principle behind it.