Likelihood principle
In statistics, the likelihood principle is the proposition that, given a statistical model, all the evidence in a sample relevant to model parameters is contained in the likelihood function. A likelihood function arises from a probability density function considered as a function of its distributional parameterization argument. For example, consider a model which gives the probability density function formula_1 of observable random variable formula_2 as a function of a parameter formula_3 Then for a specific value formula_4 of formula_5 the function formula_6 is a likelihood function of formula_7 it gives a measure of how "likely" any particular value  of formula_8 is, if we know that formula_9 has the value formula_10 The density function may be a density with respect to counting measure, i.e. a probability mass function. Two likelihood functions are "equivalent" if one is a scalar multiple of the other. The likelihood principle is this: All information from the data that is relevant to inferences about the value of the model parameters is in the equivalence class to which the likelihood function belongs. The strong likelihood principle applies this same criterion to cases such as sequential experiments where the sample of data that is available results from  applying a stopping rule to the observations earlier in the experiment. Example. Suppose Then the observation that formula_16 induces the likelihood function while the observation that formula_18 induces the likelihood function The likelihood principle says that, as the data are the same in both cases, the inferences drawn about the value of formula_8 should also be the same. In addition, all the inferential content in the data about the value of formula_8 is contained in the two likelihoods, and is the same if they are proportional to one another. This is the case in the above example, reflecting the fact  that the difference between observing formula_16 and observing formula_18 lies not in the actual data collected, nor in the conduct of the experimenter, but merely in the intentions described in the two different designs of the experiment. Specifically, in one case, the decision in advance was to try twelve times, regardless of the outcome; in the other case, the advance decision was to keep trying until three successes were observed. The inference about formula_8 should be the same, and this is reflected in the fact that the two likelihoods are proportional to each other: Except for a constant leading factor  of 220 vs. 55, the two likelihood functions are the same. This equivalence is not always the case, however. The use of frequentist methods involving p-values leads to different inferences for the two cases above, showing that the outcome of frequentist methods depends on the experimental procedure, and thus violates the likelihood principle. The law of likelihood. A related concept is the law of likelihood, the notion that the extent to which the evidence supports one parameter value or hypothesis against another is indicated by the ratio of their likelihoods, their likelihood ratio. That is, is the degree to  which the observation supports parameter value or hypothesis against . If this ratio is 1, the evidence is indifferent; if greater than 1, the evidence supports the value against ; or if less, then vice versa. In Bayesian statistics, this ratio is known as the Bayes factor, and Bayes' rule can be seen as the application of the law of likelihood to inference. In frequentist inference, the likelihood ratio is used in the likelihood-ratio test, but other non-likelihood tests are used as well. The Neyman–Pearson lemma states the likelihood-ratio test is equally statistically powerful as the most powerful  test for comparing two simple hypotheses at a given significance level, which gives a frequentist justification for the law of likelihood. Combining the likelihood principle with the law of likelihood yields the consequence that the parameter value which maximizes the likelihood function is the value which is most strongly supported by the evidence. This is the basis for the widely used method of maximum likelihood. History. The likelihood principle was first identified by that name in print in 1962 (Barnard et al., Birnbaum, and Savage et al.), but arguments for the same principle, unnamed, and the use of the principle  in applications goes back to the works of R.A. Fisher in the 1920s. The law of likelihood was identified by that name by I. Hacking (1965). More recently the likelihood principle as a general principle of inference has been championed by A. W. F. Edwards. The likelihood principle has been applied to the philosophy of science by R. Royall. Birnbaum proved that the likelihood principle follows from two more primitive and seemingly reasonable principles, the "conditionality principle" and the "sufficiency principle": However, the adequacy of Birnbaum's proof is contested ("see below"). Arguments for and against. Some widely used methods  of conventional statistics, for example many significance tests, are not consistent with the likelihood principle. Let us briefly consider some of the arguments for and against the likelihood principle. The original Birnbaum argument. Birnbaum's proof of the likelihood principle has been disputed by statisticians including Michael Evans and philosophers of science, including Deborah Mayo. Alexander Dawid points out fundamental differences between Mayo's and Birnbaum's definitions of the conditionality principle, arguing Birnbaum's proof cannot be so readily dismissed. A new proof of the likelihood principle has been provided by Greg Gandenberger that addresses some of the  counterarguments to the original proof. Experimental design arguments on the likelihood principle. Unrealized events play a role in some common statistical methods. For example, the result of a significance test depends on the -value, the probability of a result as extreme or more extreme than the observation, and that probability may depend on the design of the experiment. To the extent that the likelihood principle is accepted, such methods are therefore denied. Some classical significance tests are not based on the likelihood. The following are a simple and more complicated example of those, using a commonly cited example called "the  optional stopping problem". Suppose I tell you that I tossed a coin 12 times and in the process observed 3 heads. You might make some inference about the probability of heads and whether the coin was fair. Suppose now I tell that I tossed the coin "until" I observed 3 heads, and I tossed it 12 times. Will you now make some different inference? The likelihood function is the same in both cases: It is proportional to So according to the "likelihood principle", in either case the inference should be the same. Suppose a number of scientists are assessing the  probability of a certain outcome (which we shall call 'success') in experimental trials. Conventional wisdom suggests that if there is no bias towards success or failure then the success probability would be one half. Adam, a scientist, conducted 12 trials and obtains 3 successes and 9 failures. One of those successes was the 12th and last observation. Then Adam left the lab. Bill, a colleague in the same lab, continued Adam's work and published Adam's results, along with a significance test. He tested the null hypothesis that , the success probability, is equal to a half, versus  . If we  ignore the information that the third success was the 12th and last observation the probability of the observed result that out of 12 trials 3 or something fewer (i.e. more extreme) were successes, if is true, is which is  . Thus the null hypothesis is not rejected at the 5% significance level if we ignore the knowledge that the third success was the 12th result. However observe that this first calculation also includes 12 token long sequences that end in tails contrary to the problem statement! If we redo this calculation we realize the likelihood according to the null hypothesis must  be the probability of a fair coin landing 2 or fewer heads on 11 trials multiplied with the probability of the fair coin landing a head for the 12th trial: which is  . Now the result "is" statistically significant at the level. Charlotte, another scientist, reads Bill's paper and writes a letter, saying that it is possible that Adam kept trying until he obtained 3 successes, in which case the probability of needing to conduct 12 or more experiments is given by which is  . Now the result "is" statistically significant at the level. Note that there is no contradiction between  the latter two correct analyses; both computations are correct, and result in the same p-value. To these scientists, whether a result is significant or not does not depend on the design of the experiment, but does on the likelihood (in the sense of the likelihood function) of the parameter value being  . Results of this kind are considered by some as arguments against the likelihood principle. For others it exemplifies the value of the likelihood principle and is an argument against significance tests. Similar themes appear when comparing Fisher's exact test with Pearson's chi-squared test. The voltmeter  story. An argument in favor of the likelihood principle is given by Edwards in his book "Likelihood". He cites the following story from J.W. Pratt, slightly condensed here. Note that the likelihood function depends only on what actually happened, and not on what "could" have happened. This story can be translated to Adam's stopping rule above, as follows: Adam stopped immediately after 3 successes, because his boss Bill had instructed him to do so. After the publication of the statistical analysis by Bill, Adam realizes that he has missed a later instruction from Bill to instead conduct 12 trials , and that Bill's paper is based on this second instruction. Adam is very glad that he got his 3 successes after exactly 12 trials, and explains to his friend Charlotte that by coincidence he executed the second instruction. Later, Adam is astonished to hear about Charlotte's letter, explaining that "now" the result is significant.