There is a whole body of statistical method to deal with Multiplicity. This entails using various types of ANOVAs (depending on your hypothesis testing framework) and numerous related Post Hoc tests run after you have already done the ANOVA. Some of the most commons are Tukey's HSD, Scheffe test, REGWQ test, Dunnet test.
However, you can short cut this whole framework by using well established adjustments to your P value. So, if you want to test for P < 0.05 and you are testing 30 different but related hypothesis, your threshold of significance has to be adjusted by dividing by the number of hypotheses. In your case it would be 0.05/30. That's called the Bonferroni test. There is another similar test that figues some related compounding, and the adustment in this case is: (1 - Confidence Level)^# of hypothesis. In your case: (1 - 95%)^30. This is called the Sidak test.
Note that the two tests derive to very much the same adjustment. They just vary when you go a few decimals out.
Those two tests are very well established, easy to run, and will tell you whether you found out something by chance or not at the 95% Confidence Level by testing multiple hypothesis.