I am a little confused about what p-values mean under Fisher's signficance testing & Neyman-Pearson's hypothesis testing.
Fisher uses p-values as a continuous measure of evidence against a null hypothesis? So a p-value of 0.06 would indicate that there is no difference and the null hypothesis is true?
However, does this mean the same thing under Neyman-Pearson. I know that you have to pre-set alpha values for type 1 errors but does this affect p? Does a p-value greater than alpha indicate that there is >5% chance of a type one error occurring?