Howtosignalprocessing
- What is Neyman-Pearson detection?
- What is the difference between Neyman-Pearson's approach and Fisher's p value approach for hypothesis testing?
- What is application of Neyman-Pearson Lemma?
- Why is Neyman-Pearson Lemma the most powerful test?
What is Neyman-Pearson detection?
What is Neyman-Pearson Lemma? The Neyman-Pearson Lemma is a way to find out if the hypothesis test you are using is the one with the greatest statistical power. The power of a hypothesis test is the probability that test correctly rejects the null hypothesis when the alternate hypothesis is true.
What is the difference between Neyman-Pearson's approach and Fisher's p value approach for hypothesis testing?
A distinction frequently made between the approaches of Fisher and Neyman-Pearson is that in the latter the test is carried out at a fixed level, whereas the principal outcome of the former is the statement of a p value that may or may not be followed by a pronouncement concerning significance of the result.
What is application of Neyman-Pearson Lemma?
The Neyman–Pearson lemma is applied to the construction of analysis-specific likelihood-ratios, used to e.g. test for signatures of new physics against the nominal Standard Model prediction in proton-proton collision datasets collected at the LHC.
Why is Neyman-Pearson Lemma the most powerful test?
The Neyman Pearson Lemma is all well and good for deriving the best hypothesis tests for testing a simple null hypothesis against a simple alternative hypothesis, but the reality is that we typically are interested in testing a simple null hypothesis, such as H 0 : μ = 10 against a composite alternative hypothesis, ...
