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?

If you approached the problem this way, then you made a mistake!
What you could have done is calculate the sample size required to detect a difference of practical importance. You know it isn’t filling exactly 50, and you have some tolerable difference from that value. Maybe you can tolerate 49-51, or maybe you can tolerate 49.99-50.01. Then, if you fail to reject the null hypothesis, you know that you adequately powered your investigation to detect important differences and didn’t just fail to reject due to an inadequate sample size (e.g., it’s easy to fail to reject if you make two observations).
This approach requires you to phrase that tolerance in terms of the standard deviation. If this is not viable, then you can do a true equivalence test.
