With inferential statistics, the goal is to reject the null hypothesis. What does this mean? Do we conclude that the alternative hypothesis is correct? Why or why not?

Inferential statistics is a type of statistics that allows us to make conclusions about a population based on a sample. The null hypothesis is a statement that there is no effect or no difference in the population. It's the hypothesis that we try to invalidate with our data.

When we say "the goal is to reject the null hypothesis," it means that we are trying to provide evidence that our alternative hypothesis (the hypothesis that there is an effect or a difference) is true.

However, rejecting the null hypothesis does not automatically mean that the alternative hypothesis is correct. This is because statistical tests can only provide evidence against the null hypothesis, not prove the alternative hypothesis.

For example, if we reject the null hypothesis that a drug has no effect on a disease, it doesn't mean that the drug definitely works. There could be other factors at play causing the observed effect.

So, while rejecting the null hypothesis can provide strong evidence for the alternative hypothesis, it does not conclusively prove it. We can only say that the results are statistically significant and that they suggest the alternative hypothesis may be true.