Practical implication of failing to reject a null hypothesis Consider a scenario where you are trying to measure a dosage of a medicine. The machine is calibrated to fill a mean dosage of 50mg. But for a reason you believe that machine's calibration is off. For the above mentioned example null and alternative hypothesis will be as follows: 
null: On the average, the dosage sold is 50 mg 
alternative: On the average, the dosage sold is not 50 mg 
Upon conducting the experiment p-value is greater than say 0.05 for 95% significance level. The conclusion of this test would then be "fail to reject null hypothesis." But practically can I say that the machine is calibrated and is filling a mean dosage of 50mg?

This seems like a situation where the failure is relying on null hypothesis significance testing in the first place.
Monitoring that a machine is well calibrated is an estimation problem. You might do better to control accuracy (margin of error) of the dosage rather than the type I error. A type I error in this situation is unnecessarily stopping the production line for maintenance. A type II error is continuing to manufacture medicine with the wrong dosage. The cost of a false negative is much higher than a false positive because, if the dose doesn't match the label, patients could be given incorrect treatment.
This example also seems somewhat naive because drug manufacturing is regulated*. The FDA might not accept the failure to reject a null hypothesis as sufficient evidence of no mis-calibration.

*

*Current Good Manufacturing Practice (CGMP) Regulations
