If you conduct a hypothesis test, you define a null hypothesis - that is the condition you assume to be true unless the data suggests otherwise. You also define an alternate hypothesis which is whatever you are trying to prove.
When you carry out your test, you calculate a p-value, which is the probability that the null hypothesis is true.
Convention says that if the p-value (the level of significance) is above 5% i.e. 0.05, we haven't proved what we were trying to prove so we have to go along with the null hypothesis.
Putting it another way, if we want to show that the null hypothesis is wrong, we have to take a risk because we only work with samples. We accept a risk of 5% but no more than that. If the p-value is higher than 5% (0.05), we would be taking a greater risk than we want to if we reject the null hypothesis - so we don't.