New revision of How to become a Bayesian in eight easy steps

Quentin, Fabian, Peter, Beth and I recently resubmitted our manuscript titled “How to become a Bayesian in eight easy steps: An annotated reading list” that we initially submitted earlier this year. You can find an updated preprint here. The reviewer comments were pleasantly positive (and they only requested relatively minor changes), so I don’t expect we’ll have another revision. In the revised manuscript we include a little more discussion of the conceptual aspect of Bayes factors (in the summary of source 4), some new discussion on different Bayesian philosophies of how analysis should be done (in the introduction of the “Applied” section) and a few additions to the “Further reading” appendix, among other minor typographical corrections.

This was quite a minor revision. The largest change to the paper by far is our new short discussion on different Bayesian philosophies, which mainly revolve around the (ever-controversial!) issue of hypothesis testing. There is an understandable desire from users of statistics for a unitary set of rules and regulation–a simple list of procedures to follow–where if you do all the right steps you won’t piss off that scrupulous methods guy down the hall from you. Well, as it happens, statistics isn’t like that and you’ll never get that list. Statistics is not just a means to an end, as many substantive researchers tend to think, but an active scientific field itself. Statistics, like any field of study, is a human endeavor that has all sorts of debates and philosophical divides.

Rather than letting these divides turn you off from learning Bayes, I hope they prepare you for the vast analytic viewpoints you will likely encounter as Bayesian analyses become more mainstream. And who knows, maybe you’ll even feel inspired to approach your own substantive problems with a new frame of mind.  Here is an excerpt from our discussion:

Before moving on to our final four highlighted sources, it will be useful if readers consider some differences in perspective among practitioners of Bayesian statistics. The application of Bayesian methods is very much an active field of study, and as such, the literature contains a multitude of deep, important, and diverse viewpoints on how data analysis should be done, similar to the philosophical divides between Neyman–Pearson and Fisher concerning proper application of classical statistics (see Lehmann, 1993). The divide between subjective Bayesians, who elect to use priors informed by theory, and objective Bayesians, who instead prefer “uninformative” or default priors, has already been mentioned throughout the Theoretical sources section above.


A second division of note exists between Bayesians who see a place for hypothesis testing in science, and those who see statistical inference primarily as a problem of estimation. ….

You’ll have to check out the paper to see how the rest of this discussion goes (see page 10).   🙂

Understanding Bayes: How to cheat to get the maximum Bayes factor for a given p value

OR less click-baity: What is the maximum Bayes factor you can get for a given p value? (Obvious disclaimer: Don’t cheat)

Starting to use and interpret Bayesian statistics can be hard at first. A recent recommendation that I like is from Zoltan Dienes and Neil Mclatchie, to “Report a B for every p.” Meaning, for every p value in the paper report a corresponding Bayes factor. This way the psychology community can start to build an intuition about how these two kinds of results can correspond. I think this is a great way to start using Bayes. And if as time goes on you want to flush those ps down the toilet, I won’t complain.

Researchers who start to report both Bayesian and frequentist results often go through a phase where they are surprised to find that their p<.05 results correspond to weak Bayes factors. In this Understanding Bayes post I hope to pump your intuitions a bit as to why this is the case. There is, in fact, an absolute maximum Bayes factor for a given p value. There are also other soft maximums it can achieve for different classes of prior distributions. And these maximum BFs may not be as high as you expect.

Absolute Maximum

The reason for the absolute maximum is actually straightforward. The Bayes factor compares how accurately two or more competing hypotheses predict the observed data. Usually one of those hypotheses is a point null hypothesis, which says there is no effect in the population (however defined). The alternative can be anything you like. It could be a point hypothesis motivated by theory or that you take from previous literature (uncommon), or it can be a (half-)normal (or other) distribution centered on the null (more common), or anything else. In any case, the fact is that to achieve the absolute maximum Bayes factor for a given p value you have to cheat. In real life you can never reach the absolute maximum in a normal course of analysis so its only use is as a benchmark illustration.

You have to make your alternative hypothesis the exact point hypothesis that maximizes the likelihood of the data. The likelihood function ranks all the parameter values by how well they predict the data, so if you make your point hypothesis equal to the mode of the likelihood function, it means that no other hypothesis or population parameter could make the data more likely. This illicit prior is known as the oracle prior, because it is the prior you would choose if you could see the result ahead of time. So in the figure below, the oracle prior would correspond to the high dot on the curve at the mode, and the null hypothesis is the lower dot on the curve. The Bayes factor is then just the ratio of these heights.

When you are doing a t-test, for example, the maximum of the likelihood function is simply the sample mean. So in this case, the oracle prior is a point hypothesis at exactly the sample mean. Let’s assume that we know the population SD=10, so we’re only interested in the population mean. We collect 100 participants and the sample mean we get is 1.96. Our z score in this case is

z = mean / standard error = 1.96 / (10/√100) = 1.96.

This means we obtain a p value of exactly .05. Publication and glory await us. But, in sticking with our B for every p mantra, we decide to calculate an oracle Bayes factor just to be complete. This can easily be done in R using the following 1 line of code:

dnorm(1.96, 1.96, 1)/dnorm(1.96, 0, 1)

And the answer you get is BF = 6.83. This is the absolute maximum Bayes factor you can possibly get for a p value that equals .05 in a t test (you get similar BFs for other types of tests). That is the amount of evidence that would bring a neutral reader who has prior probabilities of 50% for the null and 50% for the alternative to posterior probabilities of 12.8% for the null and 87.2% for the alternative. You might call that moderate evidence depending on the situation. For p of .01, this maximum increases to ~27.5, which is quite strong in most cases. But these values are for the best case ever, where you straight up cheat. When you can’t blatantly cheat the results are not so good.

Soft Maximum

Of course, nobody in their right mind would accept your analysis if you used an oracle prior. It is blatant cheating — but it gives a good benchmark. For p of .05 and the oracle prior, the best BF you can ever get is slightly less than 7. If you can’t blatantly cheat by using an oracle prior, the maximum Bayes factor you can get obviously won’t be as high. But it may surprise you how much smaller the maximum becomes if you decide to cheat more subtly.

The priors most people use for the alternative hypothesis in the Bayes factor are not point hypotheses, but distributed hypotheses. A common recommendation is a unimodal (i.e., one-hump) symmetric prior centered on the null hypothesis value. (There are times where you wouldn’t want to use a prior centered on the null value, but in those cases the maximum BF goes back to being the BF you get using an oracle prior.) I usually recommend using normal distribution priors, and JASP software uses a Cauchy distribution which is similar but with fatter tails. Most of the time the BFs you get are very similar.

So imagine that instead of using the blatantly cheating oracle prior, you use a subtle oracle prior. Instead of a point alternative at the observed mean, you use a normal distribution and pick the scale (i.e., the SD) of your prior to maximize the Bayes factor. There is a formula for this, but the derivation is very technical so I’ll let you read Berger and Sellke (1987, especially section 3) if you’re into that sort of torture.

It turns out, once you do the math, that when using a normal distribution prior the maximum Bayes factor you can get for a value of .05 is BF = 2.1. That is the amount of evidence that would bring a neutral reader who has prior probabilities of 50% for the null and 50% for the alternative to posterior probabilities of 32% for the null and 68% for the alternative. Barely different! That is very weak evidence. The maximum normal prior BF corresponding to of .01 is BF = 6.5. That is still hardly convincing evidence! You can find this bound for any t value you like (for any t greater than 1) using the R code below:

t = 1.96
maxBF = 1/(sqrt(exp(1))*t*exp(-t^2/2))

(You can get slightly different maximum values for different formulations of problem. Another form due to Sellke, Bayarri, & Berger [2001] is 1/[-e*p*ln(p)] for p<~.4, which for p=.05 returns BF = 2.45)

You might say, “Wait no I have a directional prediction, so I will use a half-normal prior that allows only positive values for the population mean. What is my maximum BF now?” Luckily the answer is simple: Just multiply the old maximum by:

2*(1 – p/2)

So for p of .05 and .01 the maximum 1-sided BFs are 4.1 and 13, respectively. (By the way, this trick works for converting most common BFs from 2- to 1-sided.)

Take home message

Do not be surprised if you start reporting Bayes factors and find that what you thought was strong evidence based on a p value of .05 or even .01 translates to a quite weak Bayes factor.

And I think this goes without saying, but don’t try to game your Bayes factors. We’ll know. It’s obvious. The best thing to do is use the prior distribution you find most reasonable for the problem at hand and then do a robustness check by seeing how much the conclusion you draw depends on the specific prior you choose. JASP software can do this for you automatically in many cases (e.g., for the Bayesian t-test; ps check out our official JASP tutorial videos!).

R code

The following is the R code to reproduce the figure, to find the max BF for oracle priors, and to find the max BF for subtle oracle priors. Tinker with it and see how your intuitions match the answers you get!



Video: “A Bayesian Perspective of the Reproducibility Project: Psychology”

I recently gave a talk at the University of Bristol’s Medical Research Council Integrative Epidemiology Unit, titled, “A Bayesian Perspective on the Reproducibility Project: Psychology,” in which I recount the results from our recently published Bayesian reanalysis of the RPP (you can read it in PLOS ONE). In that paper Joachim Vandekerckhove and I reassessed the evidence from the RPP and found that most of the original and replication studies only managed to obtain weak evidence.

I’m very grateful to Marcus Munafo for inviting me out to give this talk. And I’m also grateful to Jim Lumsden for help organizing. We recorded the talk’s audio and synced it to a screencast of my slides, so if you weren’t there you can still hear about it. 🙂

I’ve posted the slides on slideshare, and you can download a copy of the presentation by clicking here. (It says 83 slides, but the last ~30 slides are a technical appendix prepared for the Q&A)

If you think this is interesting and you’d like to learn more about Bayes, you can check out my Understanding Bayes tutorial series and also our paper, “How to become a Bayesian in eight easy steps.”

A Bayesian perspective on the Reproducibility Project: Psychology

It is sometimes considered a paradox that the answer depends not only on the observations but on the question; it should be a platitude.

–Harold Jeffreys, 1939

Joachim Vandekerckhove (@VandekerckhoveJ) and I have just published a Bayesian reanalysis of the Reproducibility Project: Psychology in PLOS ONE (CLICK HERE). It is open access, so everyone can read it! Boo paywalls! Yay open access! The review process at PLOS ONE was very nice; we had two rounds of reviews that really helped us clarify our explanations of the method and results.

Oh and it got a new title: “A Bayesian perspective on the Reproducibility Project: Psychology.” A little less presumptuous than the old blog’s title. Thanks to the RPP authors sharing all of their data, we research parasites were able to find some interesting stuff. (And thanks Richard Morey (@richarddmorey) for making this great badge)


TLDR: One of the main takeaways from the paper is the following: We shouldn’t be too surprised when psychology experiments don’t replicate, given the evidence in the original studies is often unacceptably weak to begin with!

What did we do?

Here is the abstract from the paper:

We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors—a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis—for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable.

In the paper we try to answer four questions: 1) How much evidence is there in the original studies? 2) If we account for the possibility of publication bias, how much evidence is left in the original studies? 3) How much evidence is there in the replication studies? 4) How consistent is the evidence between (bias-corrected) original studies and replication studies?

We implement a very neat technique called Bayesian model averaging to account for publication bias in the original studies. The method is fairly technical, so I’ve put the topic in the Understanding Bayes queue (probably the next post in the series). The short version is that each Bayes factor consists of eight likelihood functions that get weighted based on the potential bias in the original result. There are details in the paper, and much more technical detail in this paper (Guan and Vandekerckhove, 2015). Since the replication studies would be published regardless of outcome, and were almost certainly free from publication bias, we can calculate regular (bias free) Bayes factors for them.


There are only 8 studies where both the bias mitigated original Bayes factors and the replication Bayes factors are above 10 (highlighted with the blue hexagon). That is, both experiment attempts provide strong evidence. It may go without saying, but I’ll say it anyway: These are the ideal cases. 

(The prior distribution for all Bayes factors is a normal distribution with mean of zero and variance of one. All the code is online HERE if you’d like to see how different priors change the result; our sensitivity analysis didn’t reveal any major dependencies on the exact prior used.)

The majority of studies (46/72) have both bias mitigated original and replication Bayes factors in the 1/10< BF <10 range (highlighted with the red box). These are cases where both study attempts only yielded weak evidence.


Overall, both attempts for most studies provided only weak evidence. There is a silver/bronze/rusty-metal lining, in that when both study attempts obtain only weak Bayes factors, they are technically providing consistent amounts of evidence. But that’s still bad, because “consistency” just means that we are systematically gathering weak evidence!

Using our analysis, no studies provided strong evidence that favored the null  hypothesis in either the original or replication.

It is interesting to consider the cases where one study attempt found strong evidence but another did not. I’ve highlighted these cases in blue in the table below. What can explain this?


One might be tempted to manufacture reasons that explain this pattern of results, but before you do that take a look at the figure below. We made this figure to highlight one common aspect of all study attempts that find weak evidence in one attempt and strong evidence in another: Differences in sample size. In all cases where the replication found strong evidence and the original study did not, the replication attempt had the larger sample size. Likewise, whenever the original study found strong evidence and the replication did not, the original study had a larger sample size.


Figure 2. Evidence resulting from replicated studies plotted against evidence resulting from the original publications. For the original publications, evidence for the alternative hypothesis was calculated taking into account the possibility of publication bias. Small crosses indicate cases where neither the replication nor the original gave strong evidence. Circles indicate cases where one or the other gave strong evidence, with the size of each circle proportional to the ratio of the replication sample size to the original sample size (a reference circle appears in the lower right). The area labeled ‘replication uninformative’ contains cases where the original provided strong evidence but the replication did not, and the area labeled ‘original uninformative’ contains cases where the reverse was true. Two studies that fell beyond the limits of the figure in the top right area (i.e., that yielded extremely large Bayes factors both times) and two that fell above the top left area (i.e., large Bayes factors in the replication only) are not shown. The effect that relative sample size has on Bayes factor pairs is shown by the systematic size difference of circles going from the bottom right to the top left. All values in this figure can be found in S1 Table.

Abridged conclusion (read the paper for more! More what? Nuance, of course. Bayesians are known for their nuance…)

Even when taken at face value, the original studies frequently provided only weak evidence when analyzed using Bayes factors (i.e., BF < 10), and as you’d expect this already small amount of evidence shrinks even more when you take into account the possibility of publication bias. This has a few nasty implications. As we say in the paper,

In the likely event that [the original] observed effect sizes were inflated … the sample size recommendations from prospective power analysis will have been underestimates, and thus replication studies will tend to find mostly weak evidence as well.

According to our analysis, in which a whopping 57 out of 72 replications had 1/10 < BF < 10, this appears to have been the case.

We also should be wary of claims about hidden moderators. We put it like this in the paper,

The apparent discrepancy between the original set of results and the outcome of the Reproducibility Project can be adequately explained by the combination of deleterious publication practices and weak standards of evidence, without recourse to hypothetical hidden moderators.

Of course, we are not saying that hidden moderators could not have had an influence on the results of the RPP. The statement is merely that we can explain the results reasonably well without necessarily bringing hidden moderators into the discussion. As Laplace would say: We have no need of that hypothesis.

So to sum up,

From a Bayesian reanalysis of the Reproducibility Project: Psychology, we conclude that one reason many published effects fail to replicate appears to be that the evidence for their existence was unacceptably weak in the first place.

With regard to interpretation of results — I will include the same disclaimer here that we provide in the paper:

It is important to keep in mind, however, that the Bayes factor as a measure of evidence must always be interpreted in the light of the substantive issue at hand: For extraordinary claims, we may reasonably require more evidence, while for certain situations—when data collection is very hard or the stakes are low—we may satisfy ourselves with smaller amounts of evidence. For our purposes, we will only consider Bayes factors of 10 or more as evidential—a value that would take an uninvested reader from equipoise to a 91% confidence level. Note that the Bayes factor represents the evidence from the sample; other readers can take these Bayes factors and combine them with their own personal prior odds to come to their own conclusions.

All of the results are tabulated in the supplementary materials (HERE) and the code is on github (CODE HERE).


More disclaimers, code, and differences from the old reanalysis


All of the results are tabulated in a table in the supplementary information (link), and MATLAB code to reproduce the results and figures is provided online (CODE HERE). When interpreting these results, we use a Bayes factor threshold of 10 to represent strong evidence. If you would like to see how the results change when using a different threshold, all you have to do is change the code in line 118 of the ‘bbc_main.m’ file to whatever thresholds you prefer.


Important note: The function to calculate the mitigated Bayes factors is a prototype and is not robust to misuse. You should not use it unless you know what you are doing!


A few differences between this paper and an old reanalysis:

A few months back I posted a Bayesian reanalysis of the Reproducibility Project: Psychology, in which I calculated replication Bayes factors for the RPP studies. This analysis took the posterior distribution from the original studies as the prior distribution in the replication studies to calculate the Bayes factor. So in that calculation, the hypotheses being compared are: H_0 “There is no effect” vs. H_A “The effect is close to that found by the original study.” It also did not take into account publication bias.

This is important: The published reanalysis is very different from the one in the first blog post.

Since the posterior distributions from the original studies were usually centered on quite large effects, the replication Bayes factors could fall in a wide range of values. If a replication found a moderately large effect, comparable to the original, then the Bayes factor would very largely favor H_A. If the replication found a small-to-zero effect (or an effect in the opposite direction), the Bayes factor would very largely favor H_0. If the replication found an effect in the middle of the two hypotheses, then the Bayes factor would be closer to 1, meaning the data fit both hypotheses equally bad. This last case happened when the replications found effects in the same direction as the original studies but of smaller magnitude.

These three types of outcomes happened with roughly equal frequency; there were lots of strong replications (big BF favoring H_A), lots of strong failures to replicate (BF favoring H_0), and lots of ambiguous results (BF around 1).

The results in this new reanalysis are not as extreme because the prior distribution for H_A is centered on zero, which means it makes more similar predictions to H_0 than the old priors. Whereas roughly 20% of the studies in the first reanalysis were strongly in favor of H_0 (BF>10), that did not happen a single time in the new reanalysis. This new analysis also includes the possibility of a biased publication processes, which can have a large effect on the results.

We use a different prior so we get different results. Hence the Jeffreys quote at the top of the page.



Edwards, Lindman, and Savage (1963) on why the p-value is still so dominant

Below is an excerpt from Edwards, Lindman, and Savage (1963, pp. 236-7), on why p-value procedures continue to be dominant in the empirical sciences even after it has been repeatedly shown to be an incoherent and nonsensical statistic (note: those are my choice of words, the authors are very cordial in their commentary). The age of the article shows in numbers 1 and 2, but I think it is still valuable commentary; Numbers 3 and 4 are still highly relevant today.

From Edwards, Lindman, and Savage (1963, pp. 236-7):

If classical significance tests have rather frequently rejected true null hypotheses without real evidence, why have they survived so long and so dominated certain empirical sciences ? Four remarks seem to shed some light on this important and difficult question.

1. In principle, many of the rejections at the .05 level are based on values of the test statistic far beyond the borderline, and so correspond to almost unequivocal evidence [i.e., passing the interocular trauma test]. In practice, this argument loses much of its force. It has become customary to reject a null hypothesis at the highest significance level among the magic values, .05, .01, and .001, which the test statistic permits, rather than to choose a significance level in advance and reject all hypotheses whose test statistics fall beyond the criterion value specified by the chosen significance level. So a .05 level rejection today usually means that the test statistic was significant at the .05 level but not at the .01 level. Still, a test statistic which falls just short of the .01 level may correspond to much stronger evidence against a null hypothesis than one barely significant at the .05 level. …

2. Important rejections at the .05 or .01 levels based on test statistics which would not have been significant at higher levels are not common. Psychologists tend to run relatively large experiments, and to get very highly significant main effects. The place where .05 level rejections are most common is in testing interactions in analyses of variance—and few experimenters take those tests very seriously, unless several lines of evidence point to the same conclusions. [emphasis added]

3. Attempts to replicate a result are rather rare, so few null hypothesis rejections are subjected to an empirical check. When such a check is performed and fails, explanation of the anomaly almost always centers on experimental design, minor variations in technique, and so forth, rather than on the meaning of the statistical procedures used in the original study.

4. Classical procedures sometimes test null hypotheses that no one would believe for a moment, no matter what the data […] Testing an unbelievable null hypothesis amounts, in practice, to assigning an unreasonably large prior probability to a very small region of possible values of the true parameter. […]The frequent reluctance of empirical scientists to accept null hypotheses which their data do not classically reject suggests their appropriate skepticism about the original plausibility of these null hypotheses. [emphasis added]



Edwards, W., Lindman, H., & Savage, L. J. (1963). Bayesian statistical inference for psychological research. Psychological review, 70(3), 193-242.

Question: Why do we settle for 80% power? Answer: We’re confused.

Coming back to the topic of my previous post, about how we must draw distinct conclusions from different hypothesis test procedures, I’d like to show an example of how these confusions might actually arise in practice. The following example comes from Royall’s book (you really should read it), and questions why we settle for a power of only 80%. It’s a question we’ve probably all thought about at some point. Isn’t 80% power just as arbitrary as p-value thresholds? And why should we settle for such a large probability of error before we even start an experiment?

From Royall (1997, pp. 109-110):

Why is a power of only 0.80 OK?

We begin with a mild peculiarity — why is it that the Type I error rate α is ordinarily required to be 0.05 or 0.01, but a Type II error rate as large as 0.20 is regularly adopted? This often occurs when the sample size for a clinical trial is being determined. In trials that compare a new treatment to an old one, the ‘null’ hypothesis usually states that the new treatment is not better than the old, while the alternative states that it is. The specific alternative value chosen might be suggested by pilot studies or uncontrolled trials that preceded the experiment that is now being planned, and the sample size is determined [by calculating power] with α = 0.05 and β = 0.20. Why is such a large value of β acceptable? Why the severe asymmetry in favor of α? Sometimes, of course, a Type I error would be much more costly than a Type II error would be (e.g. if the new treatment is much more expensive, or if it entails greater discomfort). But sometimes the opposite is true, and we never see studies proposed with α = 0.20 and β = 0.05. No one is satisfied to report that ‘the new treatment is statistically significantly better than the old (p ≤ 0.20)’.

Often the sample-size calculation is first made with β = α = 0.05. But in that case experimenters are usually quite disappointed to see what large values of n are required, especially in trials with binomial (success/failure) outcomes. They next set their sights a bit lower, with α = 0.05 and β = 0.10, and find that n is still ‘too large’. Finally they settle for α = 0.05 and β = 0.20.

Why do they not adjust α and settle for α = 0.20 and β = 0.05? Why is small α a non-negotiable demand, while small β is only a flexible desideratum? A large α would seem to be scientifically unacceptable, indicating a lack of rigor, while a large β is merely undesirable, an unfortunate but sometimes unavoidable consequence of the fact that observations are expensive or that subjects eligible for the trial are hard to find and recruit. We might have to live with a large β, but good science seems to demand that α be small.

What is happening is that the formal Neyman-Pearson machinery is being used, but it is being given a rejection-trial interpretation (Emphasis added). The quantities α and β are not just the respective probabilities of choosing one hypothesis when the other is true; if they were, then calling the first hypothesis H2 and the second H1 would reverse the roles of α and β, and α = 0.20, β = 0.05 would be just as satisfactory for the problem in its new formulation as α = 0.05 and β = 0.20 were in the old one. The asymmetry arises because the quantity α is being used in the dual roles that it plays in rejection trials — it is both the probability of rejecting a hypothesis when that hypothesis is true and the measure of strength of the evidence needed to justify rejection. Good science demands small α because small α is supposed to mean strong evidence. On the other hand, the Type II error probability β is being interpreted simply as the probability of failing to find strong evidence against H1 when the alternative H2 is true (Emphasis added. Recall Fisher’s quote about the impossibility of making Type II errors since we never accept the null.) … When observations are expensive or difficult to obtain we might indeed have to live with a large probability of failure to find strong evidence. In fact, when the expense or difficulty is extreme, we often decide not to do the experiment at all, thereby accpeting values of α = 0 and β = [1].

— End excerpt.

So there we have our confusion, which I alluded to in the previous post. We are imposing rejection-trial reasoning onto the Neyman-Pearson decision framework. We accept a huge β because we interpret our results as a mere failure (to produce strong enough evidence) to reject the null, when really our results imply a decision to accept the ‘null’. Remember, with NP we are always forced to choose between two hypotheses — we can never abstain from this choice because the respective rejection regions for H1 and H2 encompass the entire sample space by definition; that is, any result obtained must fall into one of the rejection regions we’ve defined. We can adjust either α or β (before starting the experiment) as we see fit, based on the relative costs of these errors. Since neither hypothesis is inherently special, adjusting α is as justified as adjusting β and neither has any bearing on the strength of evidence from our experiment.

And surely it doesn’t matter which hypothesis is defined as the null, because then we would just switch the respective α and β — that is, H1 and H2 can be reversed without any penalty in the NP framework. Who cares which hypothesis gets the label 1 or 2?

But imagine the outrage (and snarky blog posts) if we tried swapping out the null hypothesis with our pet hypothesis in a rejection trial. Would anybody buy it if we tried to accept our pet hypothesis simply based on a failure to reject it? Of course not, because that would be absurd. Failing to find strong evidence against a single hypothesis has no logical implication that we have found evidence for that hypothesis. Fisher was right about this one. And this is yet another reason NP procedures and rejection trials don’t mix.

However, when we are using concepts of power and Type II errors, we are working with NP procedures which are completely symmetrical and have no concept of strength of evidence per se. Failure to reject the null hypothesis has the exact same meaning as accepting the null hypothesis — they are simply different ways to say the same thing.  If what you want is to measure evidence, fine; I think we should be measuring evidence in any case. But then you don’t have a relevant concept of power, as Fisher has reiterated time and time again. If you want to use power to help plan experiments (as seems to be recommended just about everywhere you look) then you must cast aside your intuitions about interpreting observations from that experiment as evidence. You must reject the rejection trial and reject notions of statistical evidence. 

Or don’t, but then you’re swimming in a sea of confusion.



Royall, R. (1997). Statistical evidence: a likelihood paradigm (Vol. 71). CRC press.

Are all significance tests made of the same stuff?

No! If you are like most of the sane researchers out there, you don’t spend your days and nights worrying about the nuances of different statistical concepts. Especially ones as traditional as these. But there is one concept that I think we should all be aware of: P-values mean very different things to different people. Richard Royall (1997, p. 76-7) provides a smattering of different possible interpretations and fleshes out the arguments for why these mixed interpretations are problematic (much of this post comes from his book):

In the testing process the null hypothesis either is rejected or is not rejected. If the null hypothesis is not rejected, we will say that the data on which the test is based do not provide sufficient evidence to cause rejection. (Daniel, 1991, p. 192)

A nonsignificant result does not prove that the null hypothesis is correct — merely that it is tenable — our data do not give adequate grounds for rejecting it. (Snedecor and Cochran, 1980, p. 66)

The verdict does not depend on how much more readily some other hypothesis would explain the data. We do not even start to take that question seriously until we have rejected the null hypothesis. …..The statistical significance level is a statement about evidence… If it is small enough, say p = 0.001, we infer that the result is not readily explained as a chance outcome if the null hypothesis is true and we start to look for an alternative explanation with considerable assurance. (Murphy, 1985, p. 120)

If [the p-value] is small, we have two explanations — a rare event has happened, or the assumed distribution is wrong. This is the essence of the significance test argument. Not to reject the null hypothesis … means only that it is accepted for the moment on a provisional basis. (Watson, 1983)

Test of hypothesis. A procedure whereby the truth or falseness of the tested hypothesis is investigated by examining a value of the test statistic computed from a sample and then deciding to reject or accept the tested hypothesis according to whether the value falls into the critical region or acceptance region, respectively. (Remington and Schork, 1970, p. 200)

Although a ‘significant’ departure provides some degree of evidence against a null hypothesis, it is important to realize that a ‘nonsignificant’ departure does not provide positive evidence in favour of that hypothesis. The situation is rather that we have failed to find strong evidence against the null hypothesis. (Armitage and Berry, 1987, p. 96)

If that value [of the test statistic] is in the region of rejection, the decision is to reject H0; if that value is outside the region of rejection, the decision is that H0 cannot be rejected at the chosen level of significance … The reasoning behind this decision process is very simple. If the probability associated with the occurance under the null hypothesis of a particular value in the sampling distribution is very small, we may explain the actual occurrence of that value in two ways; first we may explain it by deciding that the null hypothesis is false or, second, we may explain it by deciding that a rare and unlikely event has occurred. (Siegel and Castellan, 1988, Chapter 2)

These all mix and match three distinct viewpoints with regard to hypothesis tests: 1) Neyman-Pearson decision procedures, 2) Fisher’s p-value significance tests, and 3) Fisher’s rejection trials (I think 2 and 3 are sufficiently different to be considered separately). Mixing and matching them is inappropriate, as will be shown below. Unfortunately, they all use the same terms so this can get confusing! I’ll do my best to keep things simple.

1. Neyman-Pearson (NP) decision procedure:
Neyman describes it thusly:

The problem of testing a statistical hypothesis occurs when circumstances force us to make a choice between two courses of action: either take step A or take step B… (Neyman 1950, p. 258)

…any rule R prescribing that we take action A when the sample point … falls within a specified category of points, and that we take action B in all other cases, is a test of a statistical hypothesis. (Neyman 1950, p. 258)

The terms ‘accepting’ and ‘rejecting’ a statistical hypothesis are very convenient and well established. It is important, however, to keep their exact meaning in mind and to discard various additional implications which may be suggested by intuition. Thus, to accept a hypothesis H means only to take action A rather than action B. This does not mean that we necessarily believe that the hypothesis H is true. Also if the application … ‘rejects’ H, this means only that the rule prescribes action B and does not imply that we believe that H is false. (Neyman 1950, p. 259)

So what do we take from this? NP testing is about making a decision to choose H0 or H1, not about shedding light on the truth of any one hypothesis or another. We calculate a test statistic, see where it lies with regard to our predefined rejection regions, and make the corresponding decision. We can assure that we are not often wrong by defining Type I and Type II error probabilities (α and β) to be used in our decision procedure. According to this framework, a good test is one that minimizes these long-run error probabilities. It is important to note that this procedure cannot tell us anything about the truth of hypotheses and does not provide us with a measure of evidence of any kind, only a decision to be made according to our criteria. This procedure is notably symmetric — that is, we can either choose H0 or H1.

Test results would look like this:

α and β were prespecified -based on relevant costs associated with the different errors- for this situation at yadda yadda yadda. The test statistic (say, t=2.5) falls inside the rejection region for H0 defined as t>2.0 so we reject H0 and accept H1.” (Alternatively, you might see “p < α = x so we reject H0. The exact value of p is irrelevant, it is either inside or outside of the rejection region defined by α. Obtaining a p = .04 is effectively equivalent to p = .001 for this procedure, as is obtaining a result very much larger than the critical t above.)

2. Fisher’s p-value significance tests 

Fisher’s first procedure is only ever concerned with one hypothesis- that being the null. This procedure is not concerned with making decisions (and when in science do we actually ever do that anyway?) but with measuring evidence against the hypothesis. We want to evaluate ‘the strength of evidence against the hypothesis’ (Fisher, 1958, p.80) by evaluating how rare our particular result (or even bigger results) would be if there were really no effect in the study. Our objective here is to calculate a single number that Fisher called the level of significance, or the p-value. Smaller p is more evidence against the hypothesis than larger p. Increasing levels of significance* are often represented** by more asterisks*** in tables or graphs. More asterisks mean lower p-values, and presumably more evidence against the null.

What is the rationale behind this test? There are only two possible interpretations of our low p: either a rare event has occurred, or the underlying hypothesis is false. Fisher doesn’t think the former is reasonable, so we should assume the latter (Bakan, 1966).

Note that this procedure is directly trying to measure the truth value of a hypothesis. Lower ps indicate more evidence against the hypothesis. This is based on the Law of Improbability, that is,

Law of Improbability: If hypothesis A implies that the probability that a random variable X takes on the value x is quite small, say p(x), then the observation X = x is evidence against A, and the smaller p(x), the stronger the evidence. (Royall, 1997, p. 65)

In a future post I will attempt to show why this law is not a valid indicator of evidence. For the purpose of this post we just need to understand the logic behind this test and that it is fundamentally different from NP procedures. This test alone does not provide any guidance with regard to taking action or making a decision, it is intended as a measure of evidence against a hypothesis.

Test results would look like this:

The present results obtain a t value of 2.5, which corresponds to an observed p = .01**. This level of significance is very small and indicates quite strong evidence against the hypothesis of no difference.

3. Fisher’s rejection trials

This is a strange twist on both of the other procedures above, taking elements from each to form a rejection trial. This test is a decision procedure, much like NP procedures, but with only one explicitly defined hypothesis, a la p-value significance tests. The test is most like what psychologists actually use today, framed as two possible decisions, again like NP, but now they are framed in terms of only one hypothesis. Rejection regions are back too, defined as a region of values that have small probability under H0 (i.e., defined by a small α). It is framed as a problem of logic, specifically,

…a process analogous to testing a proposition in formal logic via the argument known as modus tollens, or ‘denying the consequent’: if A implies B, then not-B implies not-A. We can test A by determining whether B is true. If B is false, then we conclude that A is false. But, on the other hand, if B is found to be true we cannot conclude that A is true. That is, A can be proven false by such a test but it cannot be proven true — either we disprove A or we fail to disprove it…. When B is found to be true, so that A survives the test, this result, although not proving A, does seem intuitively to be evidence supporting A. (Royall, 1997, p. 72)

An important caveat is that these tests are probabilistic in nature, so the logical implications aren’t quite right. Nevertheless, rejection trials are what Fisher referred to when he famously said,

Every experiment may be said to exist only in order to give the facts a chance of disproving the null hypothesis… The notion of an error of the so-called ‘second kind,’ due to accepting the null hypothesis ‘when it is false’ … has no meaning with reference to simple tests of significance. (Fisher, 1966)

So there is a major difference from NP — With rejection trials you have a single hypothesis (as opposed to 2) combined with decision rules of “reject the H0 or do not reject H0” (as opposed to reject H0/H1 or accept H0/H1). With rejection trials we are back to making a decision. This test is asymmetric (as opposed to NP which is symmetric) — that is, we can only ever reject H0, never accept it.

While we are making decisions with rejection trials, the decisions have a different meaning than that of NP procedures. In this framework, deciding to reject H0 implies the hypothesis is “inconsistent with the data” or that the data “provide sufficient evidence to cause rejection” of the hypothesis (Royall, 1997, p.74). So rejection trials are intended to be both decision procedures and measures of evidence. Test statistics that fall into smaller α regions are considered stronger evidence, much the same way that a smaller p-value indicates more evidence against the hypothesis. For NP procedures α is simply a property of the test, and choosing a lower one has no evidential meaning per se (although see Mayo, 1996 for a 4th significance procedure — severity testing).

Test results would look like this:

The present results obtain a t = 2.5, p = .01, which is sufficiently strong evidence against H0 to warrant its rejection.

What is the takeaway?

If you aren’t aware of the difference between the three types of hypothesis testing procedures, you’ll find yourself jumbling them all up (Gigerenzer, 2004). If you aren’t careful, you may end up thinking you have a measure of evidence when you actually have a guide to action.

Which one is correct?

Funny enough, I don’t endorse any of them. I contend that p-values never measure evidence (in either p-value procedures or rejection trials) and NP procedures lead to absurdities that I can’t in good faith accept while simultaneously endorsing them.

Why write 2000 words clarifying the nuanced differences between three procedures I think are patently worthless? Well, did you see what I said at the top referring to sane researchers?

A future post is coming that will explicate the criticisms of each procedure, many of the points again coming from Royall’s book.


Armitage, P., & Berry, G. (1987). Statistical methods in medical research. Oxford: Blackwell Scientific.

Bakan, D. (1966). The test of significance in psychological research.Psychological bulletin, 66(6), 423.

Daniel, W. W. (1991). Hypothesis testing. Biostatistics: a foundation for analysis in the health sciences5, 191.

Fisher, R. A. (1958).Statistical methods for research workers (13th ed.). New York: Hafner.

Fisher, R. A. (1966). The design of experiments (8th edn.) Oliver and Boyd.

Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics,33(5), 587-606.

Mayo, D. G. (1996). Error and the growth of experimental knowledge. University of Chicago Press.

Murphy, E. A. (1985). A companion to medical statistics. Johns Hopkins University Press.

Neyman, J. (1950). First course in probability and statistic. Published by Henry Holt, 1950.,1.

Remington, R. D., & Schork, M. A. (1970). Statistics with applications to the biological and health sciences.

Royall, R. (1997). Statistical evidence: a likelihood paradigm (Vol. 71). CRC press.

Siegel, S. C., & Castellan, J. NJ (1988). Nonparametric statistics for the behavioural sciences. New York, McGraw-Hill.

Snedecor, G. W. WG Cochran. 1980. Statistical Methods. Iowa State Univ. Press, Ames.

Watson, G. S. (1983). Hypothesis testing. Encyclopedia of Statistics in Quality and Reliability.