P-value that had only 6 cups been presented (3 of each), a perfect classification would have only yielded a "p"-value of formula_38 which would not have met this level of significance. Fisher also underlined the interpretation of "p," as the long-run proportion of values at least as extreme as the data, assuming the null hypothesis is true. In later editions, Fisher explicitly contrasted the use of the "p"-value for statistical inference in science with the Neyman–Pearson method, which he terms "Acceptance Procedures". Fisher emphasizes that while fixed levels such as 5%, 2%, and 1% are convenient, the exact "p"-value can be
P-value hypothesis testing) such as formula_9 the probability of the hypothesis given the data, or formula_10 the probability of the hypothesis being true, or formula_11 the probability of observing the given data. The "p"-value is defined as the probability, under the null hypothesis, here simply denoted by formula_4 (but is often denoted formula_13, as opposed to formula_14, which is sometimes used to represent the alternative hypothesis), of obtaining a result equal to or more extreme than what was actually observed. Depending on how it is looked at, the "more extreme than what was actually observed" can mean formula_15 (right-tail event) or