In every hypothesis test you have your null hypothesis
H0 which is the boring statement about what you have observed over long periods of time. It becomes your go-to statement.
You also have the more exciting alternate hypothesis
H1 which aims to challenge
H0, but before we sway to reject
H0 in favour of
H1, we need to mathematically establish when it is acceptable to do so.
If you observe an event taking place in a particular sample, then you need to see what the likelihood of this event occuring is if you assume
H0 to be true.
Now the kicker is that if the probability of this event happening is VERY small, i.e.
below a certain treshold which we denote as the level of significance, then it should make you think that it is extremely unlikely that this event has occured purely due to luck, and thus it is a statistically significant result because it suggests a shift away from the null hypothesis.
If this turns out to be true, i.e. p value is less than sig level, then you reject the null hypothesis as it is not longer a suitable statement. You do not accept the alternate hypothesis however either as you need to be careful.
P.S. This might help you understand a bit more:
https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/