But if data does not give us enough proof to reject that formula_3, this does not automatically prove that formula_3 is correct. If, for example, a tobacco producer wishes to demonstrate that its products are safe, it can easily conduct a test with a small sample of smokers versus a small sample of non-smokers. It is unlikely that any of them will develop lung cancer (and even if they do, the difference between the groups has to be very big in order to reject formula_3). Therefore, it is likely—even when smoking is dangerous—that our test will not reject formula_3. If formula_3 is accepted, it does not automatically follow that smoking is proven harmless. The test has insufficient power to reject formula_3, so the test is useless and the value of the "proof" of formula_3 is also null.