At my college I am doing a lot of testing, such as testing for autocorrelation, White , DW, $t$, $F$ etc.
However, I always have to look up, when to reject and when to accept for example $H_0$
Hence, I was wondering if there is a generalization for interpreting a statistical test such as: When $value$ is greater than $value$ always reject H0.
From the point of view of your professional experience, is there a pattern in all statistical tests?
In general the decision which significance level to take and when to reject depends on the prior knowledge of the researcher, which is a way to incorporate this knowledge in the test.
Read up on the p-value, which is basically what you are looking for. However, given that testing is highly dependent on the sample, the experiment or model, there are dangers when generalizing on the p-value.
A parallel thread to this has a good post on the issue: https://stats.stackexchange.com/a/61027/18459 The corresponding article can be downloaded here: http://www.psych.umn.edu/people/meehlp/113TheoreticalRisks.pdf
It's not true for all tests in statistics. Some non-parametric tests have reverse decisions.
Usually it is said that if the calculated value of a test statistic is greater than the tabulated value then we fail to reject $H_0$.
It is better to have a decision on the basis of the p-value. If the p-value is less than the chosen level of significance then we fail to reject $H_0$.
