One way to prove that this is the case is to reject the null hypothesis.
Rejecting a hypothesis does not mean an experiment was "bad" or that it didn't produce results.
The null hypothesis—which assumes that there is no meaningful relationship between two variables—may be the most valuable hypothesis for the scientific method because it is the easiest to test using a statistical analysis.
This means you can support your hypothesis with a high level of confidence.
Alternate hypothesis: The presence of a mentoring relationship influences first-generation students’ intention to remain at their university.
Hypotheses may be worded with or without a direction.Keep in mind, even if the confidence level is high, there is still a small chance the null hypothesis is not true, perhaps because the experimenter did not account for a critical factor or because of chance.This is one reason why it's important to repeat experiments.A significance test is used to determine the likelihood that the results supporting the null hypothesis are not due to chance.A confidence level of 95 percent or 99 percent is common.Some people like to form hypotheses they think are true, then try to confirm them.I think it’s generally a better route to truth to form hypotheses you think have a good chance of being false, and then try to eliminate them.Testing the null hypothesis can tell you whether your results are due to the effect of manipulating the dependent variable or due to chance.The null hypothesis states there is no relationship between the measured phenomenon (the dependent variable) and the independent variable.People don’t always use these words precisely, but in one sense they are the same thing, something you pretend is true and work out the consequences.With an assumption, you’re curious about where it leads.