You have to define what you mean by 'good' and 'very high'. These are relative terms.
For example, we could list the world countries and their inflation rates putting them into defined groups such as Very Low-Low-Medium-High-Very High. You may define very low as (say) < 0% and Very high as > 100%. Or you may decide that the groups are evenly spaced (i.e 5 equal groups of 20%). Whatever, criteria you use need to be pre-defined and stated.
There is often confusion between hypothesis and null hypothesis significance testing (NHST). NHST as a concept is simple to understand, but difficult in execution. The difficulty is in interpretation.
A hypothesis is a logical statement of conditions and conclusion. A hypothesis need not be proved, only stated. Proof or demonstration takes more effort than performing an experiment and obtaining a statistical value.
Proof requires a clear statement of conditions, "given" and an objective, "to prove." One must make clear what constitutes proof. Proof is not a statistical test, but can be part of the construction of evidence.
Generally-speaking, a hypothesis is a tentative statement about causal relationships between variables. A 'statistical' hypothesis can contain more than two variables and the null hypothesis is essentially a potential 'error' statement of no affect. The statistical hypothesis is commonly 'directional'.
This means that either a positive or negative value is stated. Statistically, the accuracy of the value statement is pre-determined through stating significance and/or probability levels. The attached chapter may assist.
Apologies all - I meant the 'research' hypothesis is commonly directional - whereas the statistical hypothesis is another name for the null hypothesis.