Selecting Null and Alternate Hypothesis The following is an example of a hypothesis testing from an introductory stats book.

A medical research team has been given the task of evaluating a new
  laser treatment for certain types of tumors. Consider the following
  two scenarios:
Scenario 1: The current standard treatment is considered reasonable
  and safe by the medical community, is not costly, has no major side
  effects, and has a known success rate of 0.85 (or 85%).
Scenario 2: The current standard treatment sometimes has serious side
  effects, is costly, and has a known success rate of 0.30 (or 30%).

The solution provided by the books is as follows:

In the first scenario, the research question of interest would
  probably be “Does the new treatment have a higher success rate than
  the standard treatment?” Unless there is convincing evidence that the
  new treatment has a higher success rate, it is unlikely that current
  medical practice would change. With p representing the true proportion
  of success for the laser treatment, the following hypotheses would be
  tested:
H0: p=0.85 versus  Ha: p > 0.85
In this case, rejecting the null hypothesis would require convincing
  evidence that the success rate is higher for the new treatment.
In the second scenario, the current standard treatment does not have
  much to recommend it. The new laser treatment may be considered
  preferable because of cost or because it has fewer or less serious
  side effects, as long as the success rate for the new procedure is no
  worse than that of the standard treatment. Here, researchers might
  decide to test
H0: p=0.30 versus Ha: p<0.30

My question is why in the 2nd scenario alternative hypothesis is p<0.30 not p>0.30?
 A: H0: p=0.30 versus Ha: p<0.30 is testing if the new treatment is worse than the current treatment, vs it being the same
H0: p=0.30 versus Ha: p>0.30 is testing if the treatment is better than the current treatment.
The choice of alternative hypothesis has to do with the goal of the study. A non inferiority trial would use the former option.
A: The proposal 2 is to ensure the new method is not less effective than the existing method. The only current option in this scenario has many undesirable characteristics, so it is simpler to demonstrate that the new method improves these characteristics without compromising quality. 
In any investigation resources and risks should be adequately balanced in order to return the most informative results for the least risk. Statistics does not happen in a vacuum, there are always real world demands, limitations and restrictions. When one is planning an investigation it is necessary to fully consider all relevant issues, which may differ from field to field or even between specific applications.
In the medical research scenario presented some (far from all) of the concerns are:


*

*Ethics - the need to minimise exposure of subjects to incompletely quantified risks (although preliminary data for the new method promises improved safety this not as well tested as an established methods with extensive post-market review).

*Monetary Cost - biomedical research is not cheap and as the lower hanging fruit is picked, it is getting harder and more expensive to make incremental improvements. Trials can cost tens of thousands or multiples of that per subject, so going for a trial needing 100 vs 1000 people can make a huge difference to the affordability of the research.

*time pressure - other teams may be working on competing technologies. If you are third to market you now have to beat the new technology that beat the first one, and it may have improved side effects too. A year long study vs a 3 year one can make a huge difference

*patient care - until your product is launched patient will continue to be put at risk by the existing method and so it is ethically imperative that it be improved upon as quickly as possible. How many people would suffer from the side effects from delaying for a larger trial?
So this means there are lots of reasons to test against a less stringent benchmark. Once you have decide that then the alternative hypothesis must be framed in such a way as to provide a rigorous attempt at falsification of that hypothesis. So if you want to test some thing is not worse, then that needs to be the alternative hypothesis ($H_a<0.3$), not one formulated to test a completely different hypothesis such as that it is better ($H_a>0.3$).
Of course a prudent R&D manager will forsee the day competitors will catch up and will plan for continuing to develop the evidence for the new method beyond this trial. This will be easier to do if it is bringing in revenue and is deployed in the healthcare system demonstrating real world safety and efficacy gains.
