PHILOSOPHY OF SCIENCE Bayesian inference Thomas Bayes 1702

PHILOSOPHY OF SCIENCE: Bayesian inference Thomas Bayes 1702 -1761 Zoltán Dienes, Philosophy of Psychology

Subjective probability: Personal conviction in an opinion – to which a number is assigned that obeys the axioms of probability. Probabilities reside in the mind of the individual not the external world. There are no true or objective probabilities. You can’t be criticized for your subjective probability regarding any uncertain proposition – but you must revise your probability in the light of data in ways consistent with the axioms of probability.

Subjective odds of a theory being true: How much would you just be willing to pay if theory is found to be false in compensation for a commitment from someone else to pay you one unit of money if theory is found to be true? E. g. theory: The next toss will be heads. I will pay you a pound if the next toss is heads. Will you play if I want 50 p if it is tails? 90 p? £ 1. 50?

Subjective odds of a theory being true: How much would you just be willing to pay if theory is found to be false in compensation for a commitment from someone else to pay you one unit of money if theory is found to be true? E. g. theory: The next toss will be heads. I will pay you a pound if the next toss is heads. Will you play if I want 50 p if it is tails? 90 p? £ 1. 50? Assuming the highest amount you picked is £ 1 Odds in favour of the next toss being heads = 1. NB: Odds = Prob(true)/Prob(false).

Subjective odds of a theory being true: How much would you just be willing to pay if theory is found to be false in compensation for a commitment from someone else to pay you one unit of money if theory is found to be true? Theory: There is a two-headed winged monster behind the door I will pay you a pound if we open the door and find a monster Will you play if I want 50 p if there is no monster? 25 p 0 p? ?

Subjective odds of a theory being true: How much would you just be willing to pay if theory is found to be false in compensation for a commitment from someone else to pay you one unit of money if theory is found to be true? Theory: There is a two-headed winged monster behind the door I will pay you a pound if we open the door and find a monster Will you play if I want 50 p if there is no monster? 25 p 0 p? ? Assuming you picked 0 Odds in favour of there being a monster = 0.

Odds = Prob(true)/Prob(false) Prob(true) = odds/(odds + 1) This is a notion of probability that applies to the truth of theories! (Remember objective probability does not apply to theories) So that means we can answer questions about p(H) – the probability of a hypothesis being true – and also p(H|D) – the probability of a hypothesis given data (which we cannot do on the Neyman-Pearson approach).

Axioms of probability include: P(Y and C) = P(Y|C)*P(C) = ½ P(Y|C) = 1/3 => P(Y and C) = 1/6 ALSO P(Y) = 4/6 P(C|Y) = 1/4 => P(Y and C) = 1/6

Bayes Theorem: P(H and D) = P(H|D)*P(D) P(H and D) = P(D|H)*P(H) => P(H|D)*P(D) = P(D|H)*P(H)

Bayes Theorem: P(H and D) = P(H|D)*P(D) P(H and D) = P(D|H)*P(H) => P(H|D)*P(D) = P(D|H)*P(H) => P(H|D) = P(D|H)*P(H) /P(D) . . . (1)

Bayes Theorem: P(H and D) = P(H|D)*P(D) P(H and D) = P(D|H)*P(H) => P(H|D)*P(D) = P(D|H)*P(H) => P(H|D) = P(D|H)*P(H) /P(D) . . . (1) So considering different hypotheses for the same data P(H|D) is proportional to P(D|H) * P(H) Posterior probability of the hypothesis in the light of data D Likelihood Prior probability . . . (2)

P(H|D) is proportional to Posterior P(D|H) * P(H) Likelihood Prior ÞAll the support for a theory provided by data D is captured by the likelihood. What is a likelihood? Consider an example from the Neyman-Pearson lectures: sample mean blood pressure with drug = Md sample mean blood pressure with placebo = Mp

Probability of obtaining exactly that mean difference SE Likelihood Hypothesis that “population mean = sample mean” has highest likelihood p(D|H) 0 (Md – Mp) Population values of mean difference: each value is a different H Height of the curve for a population mean of 0 gives likelihood of null hypothesis

Difference between likelihood and significance testing : p(D|H) 0 p(D|H 0) (Md – Mp) Different possible population means (H) With likelihood you are just interested in the height of the curve 0 (Md – Mp) Different possible sample means With sig testing, you are interested in the area under the curve beyond that point (significance level)

Likelihood is gives a continuous graded measure of support for different hypotheses; significance testing asks you to make a black and white decision Likelihood reflects just what the data were; significance testing uses tail areas, reflecting what might have happened but did not. Likelihoods are insensitive to whether you are performing a post hoc test or a planned comparison, and how many other tests you are conducting.

P(H|D) is proportional to P(D|H) * P(H) Posterior probability of the hypothesis in the light of data D Likelihood Prior We can use Bayes’ theorem to: 1. Calculate the probable values of a parameter (“credibility intervals”) 2. Compare the relative probability of different hypotheses, e. g. how likely is the alternative hypothesis compared to the null? ( “Bayes factor”)

1. Credibility intervals We want to determine how probable different population values of e. g. (Md – Mp) are. First decide on a prior Assume some normal distribution does not violate the shape of your prior too much: i. e. you think certain values are reasonably likely, and more extreme values less likely in a symmetrical way Prior prob. Possible population values of (Md – Mp) The value you think is most likely

The spread in your values – the standard deviation - can be assigned by remembering: You should think that plus or minus one standard deviation has a 68% probability of including the actual population value You should think that plus or minus two standard deviations has a 95% probability of including the actual population value The bigger your standard deviation the more open minded you are Prior prob. Possible population values of (Md – Mp) What you think the most likely value is

If the standard deviation is infinite, you think all values are equally likely – this is called a “flat prior”. You have NO idea what the population value is likely to be. Prior prob. Possible population values of (Md – Mp) To choose a prior decide: 1. Does it have a roughly normal shape? 2. The mean of your normal (call it M 0) 3. The standard deviation of the normal (call it S 0) 4. Remember: There are no “right” answers! This is YOUR prior!

Prob Possible population values A possible prior: You think an effect of 0 is most plausible and you are virtually certain that the true effect, whatever it is, lies between – 10 and +10 M 0 = 0, S 0= 3

Likelihood Prior Possible population values You collect data from a normal population; your sample has a mean of 2. 8 and a standard error of 1. 09. Assuming your N is above 30, you can represent the likelihood as a normal distribution with a mean of 2. 8 and a standard deviation of 1. 09.

p(H|D) is proportional to p(D|H) * p(H) Posterior probability Likelihood Prior Need to multiply corresponding points on the graphs: Likelihood Prior Multiplying: 0 0 0 quite big about 0 again = posterior

Posterior = likelihood * prior (scale so that area = 1) Posterior Likelihood Prior Possible population values Note: For reasonably diffuse prior, the posterior is dominated by the likelihood, i. e. by the data

If both prior and likelihood are normal, it turns out the posterior is normal. Mean of prior = M 0 Mean of sample = Md Standard deviation of prior = S 0 Precision of prior = c 0 = 1/S 02 Precision of sample = cs = 1/SE 2 Posterior precision c 1= c 0 + cs Posterior mean M 1 = (co/c 1)*Mo + (cs/co)Md Posterior standard deviation, S 1 = sqrt(1/c 1)

95% credibility interval (also: probability interval, highest density region or HDR). Posterior 95% of area M 1 -1. 96*S 1 M 1+1. 96*S 1 Find the values of blood pressure change that include 95% of the area: M 1, plus or minus 1. 96*S 1.

95% credibility interval (also: probability interval, highest density region or HDR). Posterior Population blood pressure change 0. 5 95% of area 4. 5 Find the values of blood pressure change that include 95% of the area: You believe that there is a 95% probability of the true blood pressure change caused by the drug lies between 0. 5 and 4. 5 mm. Hg

If the prior is flat, the 95% credibility interval is the same interval as the 95% confidence interval of Neyman-Pearson. BUT different meaning: Confidence interval is associated with an objective probability: IF you repeated your experiment an indefinite number of times, the true population value would lie in the 95% confidence interval 95% of the time. However, you CANNOT make any claim about how likely THIS interval is in enclosing the true population mean You cannot really be 95% confident that the true population value lies in the 95% confidence interval Savage: I know of no good use for a confidence interval if not to be confident in it!

ALSO: Confidence interval will have to be adjusted according to how many other tests you conducted, under what conditions you planned to stop collecting data, and whether the test was planned or post hoc. Credibility interval is unaffected by all these things (EXCEPT some stopping rules – we discuss this later). The credibility interval IS affected by any prior information you had.

2. Compare the relative probability of different hypotheses, e. g. how likely is the alternative hypothesis compared to the null? ( “Bayes factor”) Bayes: P(H|D) is proportional to P(D|H) * P(H) => P(H 1|D) is proportional to P(D|H 1) * P(H 1) P(H 0|D) is proportional to P(D|H 0) * P(H 0)

2. Compare the relative probability of different hypotheses, e. g. how likely is the alternative hypothesis compared to the null? ( “Bayes factor”) Bayes: P(H|D) is proportional to P(D|H) * P(H) => P(H 1|D) is proportional to P(D|H 1) * P(H 1) P(H 0|D) is proportional to P(D|H 0) * P(H 0) => P(H 1|D) / P(H 0|D) = posterior odds = P(D|H 1)/P(D|H 0) * P(H 1)/P(H 0) likelihood ratio * prior odds The likelihood ratio (in this case) is called the “Bayes factor” (B) in favour of the alternative hypothesis

Consider a theory you might be testing in your project. Prior odds of theory being true: How much would you just be willing to pay if theory is found to be false in compensation for a commitment from someone else to pay you one unit of money if theory is found to be true? Experimental results tell you by how much to increase your odds (B) Not a black and white decision like in significance testing. If B = about 1, experiment was not sensitive. (Automatically get a notion of sensitivity; contrast: just relying on p values in significance testing. )

EXAMPLE WITH REAL DATA: Sheldrake’s (1981) theory of morphic resonance

EXAMPLE WITH REAL DATA: Sheldrake’s (1981) theory of morphic resonance - Any system by virtue of assuming a particular form, becomes associated with a “morphic field” - The morphic field then plays a causal role in the development and maintenance of future systems, acting perhaps instantaeously through space and without decay through time - The field guides future systems to take similar forms - The effect is stronger the more similar the future system is to the system that generated the field - The effect is stronger the more times a form has been assumed by previous similar systems - The effect occurs at all levels of organization

Nature editorial by John Maddox 1981: The “book is the best candidate for burning there has been in many years . . . Sheldrake’s argument is pseudo-science . . . Hypotheses can be dignified as theories only if all aspects of them can be tested. ” Wolpert, 1984: “. . . It is possible to hold absurd theories which are testable, but that does not make them science. Consider the hypothesis that the poetic Muse resides in tiny particles contained in meat. This could be tested by seeing if eating more hamburgers improved one’s poetry”

Repetition priming Subjects identify a stimulus more quickly or accurately with repeated presentation of the stimulus Lexical decision Subjects decide whether a presented letter string makes a meaningful English word or not (in the order actually presented). Two aspects of repetition priming are consistent with an explanation that involves morphic resonance: Durability, stimulus specificity Unique prediction of morphic resonance: Should get repetition priming between separate subjects! (ESP)

Design: Stimuli: shared+unique . . . Subject no: 1 2. . 9 10, . . . boosters resonator . . . Subject type: resonator

Design: Stimuli: shared+unique . . . Subject no: 1 2. . 9 10, . . . boosters resonator . . . Subject type: resonator - There were 10 resonators in total with nine boosters between each. Resonators were assigned randomly in advance to their position in the sequence. - The shared stimuli received morphic resonance at ten times the rate as the unique stimuli - There was a distinctive experimental context (white noise, essential oil of ylang, stimuli seen through a chequerboard pattern)

Design: Stimuli: shared+unique . . . Subject no: 1 2. . 9 10, . . . boosters resonator . . . Subject type: resonator - There were 10 resonators in total with nine boosters between each - The shared stimuli received morphic resonance at ten times the rate as the unique stimuli - There was a distinctive experimental context (white noise, essential oil of ylang, stimuli seen through a chequerboard pattern) Prediction of theory of morphic resonance: The resonators should become progressively faster on the shared as compared to the unique stimuli

Data for words. slope (ms/resonator) = 0. 9 , SE = 3. 1 Neyman-Pearson: p = 0. 9, ns

Data for words. slope (ms/resonator) = - 5. 0 , SE = 1. 5 Neyman-Pearson: p = 0. 009 significant

Morphic resonance theory: (shared – unique) RT should be more negative in the same rather than different context. 6 more resonators run in same context (ylang etc); 6 in different context No difference between same and different contexts. Overall slope = 2. 8 ms/resonator SE = 1. 09, p =. 018, still sig

Bayesian analysis P(H 1|D) / P(H 0|D) = P(D|H 1)/P(D|H 0) * P(H 1)/P(H 0) posterior odds = likelihood ratio * prior odds Need to determine p(D|H 0) and p(D|morphic resonance exists) p(D|H 0): p(D|H 0) H 0: population slope = 0 p(D|H 0) is just height of normal curve at a z of (mean slope)/(SE slope) i. e. at a z of -2. 8/1. 09 = -2. 6. The height here is. 013 -2. 6 0 z

p(D|morphic resonance) = ? Morphic resonance is consistent with a number of population slopes; in fact, at first blush, any slope > 0. Need to determine p(population slope|morphic resonance) for all slope values. Completely flat prior for positive slopes: p(populaton slope|M) 0 Possible population slope

But morphic resonance cannot allow any slope: the between-subject priming must be less than within subject priming. Within a subject, RT sped up by 20 ms with a repetition. 1 resonator = 10 boosters Each booster saw each stimulus 3 times So boosting between each resonator = 30 repetitions. So slope cannot be more than 30 x 20 = 600 ms

So slope cannot be more than 30 x 20 = 600 ms Assuming we have no preference whatsoever in thinking any of the slopes in the range from 0 to 600 ms are more likely than any other value (an implausible assumption but let’s just see the consequences): p(population slope|M) 0 600 ms Possible population slope

To go from p(population slope|M) to p(observing a slope|M) need to smear the graph by the SE of the sample. In fact, since the SE is about 1 ms this smearing is negligible in this case. p(data|M) is pretty much the same as p(population slope|M) p(data|M) SE 600 ms 0 observed slope

Assume p(data|M) is given by the distribution below, where obtained sample SE =1. 09 Since the distribution is SO long, i. e. so many observed values are possible, the probability of observing a slope in any one 1 ms interval, e. g. 2 -3 ms, is actually very small! p(data|M) SE 600 ms 0 observed slope

Assume p(data|M) is given by the distribution below, where SE =1. 09 Since the distribution is SO long, i. e. so many observed values are possible, the probability of observing a slope in any 1 ms interval, e. g. 2 -3 ms, is actually very small! Actual value = 2. 8 ms p(observing slope = 2. 8 ms| this model of morphic resonance) =. 002 So Bayes factor = p(D|M)/ p(D|H 0) =. 002/. 013 =. 15 p(data|M) SE 600 ms 0 observed slope

Posterior odds = Bayes factor * prior odds Bayes factor =. 15 Means data should REDUCE your confidence in morphic resonance and INCREASE your confidence in the null hypothesis! Contrast Neyman-Pearson in this case: p =. 018, so we reject the null hypothesis! Moral: On a Bayesian analysis, a significant result may lead one to prefer the null hypothesis even more rather than a poorly specified theory!

Contrast distributions for two theories below. Note area under the curve must always be 1. White theory is more precise than the yellow: So each possible slope in its range has a higher p than yellow. Finding data in the range 0 -150 would support white more than yellow because white more strongly predicted it. p(Slope|M) p(100|w) / p(100/y) = 4 0 150 600 Possible population slope

Bayes factor =. 15 Means data should REDUCE your confidence in morphic resonance and INCREASE your confidence in the null hypothesis! Contrast Neyman-Pearson in this case: p =. 018, so we reject the null hypothesis! Moral: On a Bayesian analysis, a significant result may lead one to prefer the null hypothesis even more than one did before over a poorly specified theory! BUT morphic resonance is not so poorly specified as in this example. The assumption that morphic resonance allows all slope values between 0 and 600 equally is implausible.

Based on studies Sheldrake has explained in terms of morphic resonance, between-subjects effects have been roughly. 005 * withinsubjects effects. So a likely value for the slope is. 005*600 = 3 ms. A rectangular distribution doesn’t capture our intuitions very well either; presumably the probability of large slopes is smaller than small slopes: p(population slope|M) 0 3 Possible population slopes To 600

With a distribution like this, Bayes factor = 16 i. e. whatever one’s prior odds in favour of morphic resonance you should multiply them by 16 in the light of these data. Contrast Neyman-Pearson: Result was significant so should categorically reject null hypothesis With Bayesian approach, if before you had a very low odds in favour of morphic resonance, they can still be very low afterwards.

NB: I ran further studies: Expt 2: 20 boosters between each resonator, 2 resonators run after each set of boosters. Nineteen resonators also run in Goetingen – could they show which word set was being boosted in Sussex? ? Expt 3: 20 boosters again, 2 resonators, one in each pair in a highly distinctive context All results flat as a pancake. Combined Bayes factor for non-word data = about 1/5 Combining with word data = about 1/16 Does not rule out morphic resonance – just changes our odds

Summary: A Bayes factor tells you how much to multiply your prior odds in the light of data. Advantages: Low sensitive experiments show up as having Bayes’ factors near 1. You are not tempted to accept the null hypothesis just because the experiment was insensitive. Penalises vague theories – data significantly different from null may still actually support null! (Compare and contrast Popper)

Disadvantages: Note the somewhat arbitrary way in which we settled on p(D|M): p(D|M) then reflects not only the data but also our subjective judgements (so it is not a true likelihood) So Bayes factor also does not reflect just the data In ideal cases, theory specifies p(D|theory) precisely; this will be rare in psychology

Likelihoods, and hence Bayes factors, are insensitive to many stopping rules: e. g. consider example from previous lecture: the proportion of women with G spot orgasm “stop when have reached a certain sample size” vs “stop when have counted a certain number of women with G stop orgasm” Makes no difference to likelihood, so makes no difference to Bayes factor (or credibility interval).

Stopping rule can be conditioned on anything in itself uninformative about the hypothesis. So: “Stop when Bayes factor = 4 OR ¼” is a fine rule “Stop when Bayes factor = 4” cannot be used. Similarly for credibility intervals: “Stop when 95% credibility interval has a width of 4 mm. HG” is a splendid rule “Stop when 95% credibility interval excludes 0” is no good. Explanation: The rule “Stop when Bayes factor = 4” means that the standardly computed likelihood no longer contains ALL the information provided by the data about the truth of the hypothesis; there is additional information in the time taken to reach that likelihood. Therefore one cannot obtain the posterior by simply multiplying the prior by the likelihood.

Summary With a Bayesian analysis you can 1. Calculate credibility intervals – the probability that a population value lies in certain intervals. 2. Calculate Bayes factor: How much more likely does the data make a theory compared to the null hypothesis 3. Both procedures encourage you to think about what size are the effects your theory specifies

The strengths of Bayesian analyses are also its weaknesses: 1. Are our subjective convictions really susceptible to the assignment of precise numbers and are they really the sorts of things that do or should follow the axioms of probability? Should papers worry about the strength of our convictions in their result sections, or just the objective reasons for why someone might change their opinions?

The strengths of Bayesian analyses are also its weaknesses: 1. Are our subjective convictions really susceptible to the assignment of precise numbers and are they really the sorts of things that do or should follow the axioms of probability? Should papers worry about the strength of our convictions in their result sections, or just the objective reasons for why someone might change their opinions? 2. Their insensitivity to stopping rules means Bayesian procedures are not guaranteed to control error probabilities (Type I, type II). (compare Mayo, 1996). 3. If you calculated 100 95% credibility intervals when the null was always true, you would expect about 5 to exclude 0. 4. A Bayesian thinks this is is just as it should be. But should she be worried?

10 measures of early toilet training are correlated with 10 measures of adult personality Out of these 100 correlations, 4 are found to be significant. Neyman Pearson: One expects about 5 to be significant by chance alone; these are weak data and do not lead one to accept any hypothesis about toilet training affecting personality. On A Bayesian analysis: These four 95% credibility intervals exclude 0. No need to take into account you also conducted 96 other tests. You have good support for four specific hypotheses concerning toilet training and personality.

But: Bayesian is not making a black and white decision; just getting a continuous measure of support. If something looks interesting she can simply decide to collect more data until she is satisfied. Also: Bayesian does not ignore all the null results in evaluating a grand theory concerning toilet training and personality; e. g. if a Freudian theory predicted ALL tested relationships, its Bayes factor in the light of the 100 correlations could be very low!

BUT you can still pick out one result that is significant – that specific hypothesis gets lots of support. SHOULD one’s confidence in that hypothesis be reduced because of all the other tests that were done? But why should what else you may or may not have done matter? If you were an editor would you publish the paper because it was good support for one of the 100 specific hypotheses? What if the author just reported testing that one correlation and constructed a plausible theory for it to put in his introduction. According to the Bayesian, there is nothing wrong with that. According to classical statistics, that is clearly cheating.