- You look at the p-value for interpreting the regression results. This p-value indicates whether there is a support for your hypothesis or not.
- p- value indicates significance level of the test. It indicates the strength of evidence. It is the calculated probability of rejecting the null hypothesis. In our data analysis, We cannot prove that the alternate hypothesis is true. we expect the null hypothesis to be false. We cannot prove that the alternative hypothesis is true but we may be able to demonstrate that the alternative is much more plausible than the null hypothesis given the data. This is usually expressed in terms of a probability value ( p-value) quantifying the strength of the evidence against the null hypothesis in favor of the alternative.
A small p-value indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis