Final answer:
Hypothetical level of certainty relies on whether the hypothesis anticipates experience and gains credibility when results align with expectations. In statistics, criteria involve set levels of significance (α) for hypothesis testing. Philosophical debates on certainty argue that absolute proof may not be necessary for justification of knowledge.
Step-by-step explanation:
The criteria for a hypothetical level of certainty involve whether the hypothesis enables us to anticipate experience in a reliable manner. When we set out to test a hypothesis, such as "The number of deformed frogs in five ponds polluted with chemical X is higher than the number of deformed frogs in five ponds without chemical X", we are looking for empirical evidence to support or refute our expectations. The criteria are not necessarily as strict for an expected result as for an unexpected one, but if the observation conforms to our expectations, the truth of the proposition is confirmed, enhancing its credibility.
For instance, in hypothesis testing within statistics, we often set a level of significance such as 1% or 5%, known as the alpha (α) value. This is the threshold for rejecting the null hypothesis in favor of the alternative hypothesis, based on the concept of statistical significance. The criterion for certainty is bridged through repeated verification under these preset conditions. A hypothesis that cannot be falsified or consistently produces expected results under set criteria gains stronger support.
In the context of philosophical skepticism, certainty is often debated, as doubt exists when a possibility cannot be ruled out. However, philosophers such as Moore suggest that certainty is not always a necessary condition for justification of knowledge. Instead, reasonable grounds to reject skeptical arguments may suffice, leading to acceptance of claims with a lesser degree of certainty than absolute proof.