Correcting bias in meta-analyses: What not to do (meta-showdown Part 1)

tl;dr: Publication bias and p-hacking can dramatically inflate effect size estimates in meta-analyses. Many methods have been proposed to correct for such bias and to estimate the underlying true effect. In a large simulation study, we studied which methods do (not) work well under which conditions, and give some recommendations what not to use.
Estimated reading time: 7 min.

It is well known that publication bias and p-hacking inflate effect size estimates from meta-analyses. In the last years, methodologists have developed an ever growing menu of statistical approaches to correct for such overestimation. However, to date it was unclear under which conditions they perform well, and what to do if they disagree. Born out of a Twitter discussion, Evan Carter, Joe Hilgard, Will Gervais and I did a large simulation project, where we compared the performance of naive random effects meta-analysis (RE), trim-and-fill (TF), p-curve, p-uniform, PET, PEESE, PET-PEESE, and the three-parameter selection model (3PSM).

Previous investigations typically looked only at publication bias or questionable research practices QRPs (but not both), used non-representative study-level sample sizes, or only compared few bias-correcting techniques, but not all of them. Our goal was to simulate a research literature that is as realistic as possible for psychology. In order to simulate several research environments, we fully crossed five experimental factors: (1) the true underlying effect, δ (0, 0.2, 0.5, 0.8); (2) between-study heterogeneity, τ (0, 0.2, 0.4); (3) the number of studies in the meta-analytic sample, k (10, 30, 60, 100); (4) the percentage of studies in the meta-analytic sample produced under publication bias (0%, 60%, 90%); and (5) the use of QRPs in the literature that produced the meta-analytic sample (none, medium, high).

This blog post summarizes some insights from our study, internally called “meta-showdown”. Check out the preprint; and the interactive app metaExplorer. The fully reproducible and reusable simulation code is on Github, and more information is on OSF.

In this blog post, I will highlight some lessons that we learned during the project, primarily focusing on what not do to when performing a meta-analysis.

Constraints on Generality disclaimer: These recommendations apply to typical sample sizes, effect sizes, and heterogeneities in psychology; other research literatures might have different settings and therefore a different performance of the methods. Furthermore, the recommendations rely on the modeling assumptions of our simulation. We went a long way to make them as realistic as possible, but other assumptions could lead to other results.

Never trust a naive random effects meta-analysis or trim-and-fill (unless you meta-analyze a set of registered reports)

If studies have no publication bias, nothing can beat plain old random effects meta-analysis: it has the highest power, the least bias, and the highest efficiency compared to all other methods. Even in the presence of some (though not extreme) QRPs, naive RE performs better than all other methods. When can we expect no publication bias? If (and, in my opinion only if) we meta-analyze a set of registered reports.

But.

In any other setting except registered reports, a consequential amount of publication bias must be expected. In the field of psychology/psychiatry, more than 90% of all published hypothesis tests are significant (Fanelli, 2011) despite the average power being estimated as around 35% (Bakker, van Dijk, & Wicherts, 2012) – the gap points towards a huge publication bias. In the presence of publication bias, naive random effects meta-analysis and trim-and-fill have false positive rates approaching 100%:

More thoughts about trim-and-fill’s inability to recover δ=0 are in Joe Hilgard’s blog post. (Note: this insight is not really new and has been shown multiple times before, for example by Moreno et al., 2009, and Simonsohn, Nelson, and Simmons, 2014).

Our recommendation: Never trust meta-analyses based on naive random effects and trim-and-fill, unless you can rule out publication bias. Results from previously published meta-analyses based on these methods should be treated with a lot of skepticism.

 

Do not use p-curve to estimate the mean of all conducted studies under heterogeneity (it is not intended to do that)

Update 2017/06/09: We had a productive exchange with Uri Simonsohn and Joe Simmons concerning what should be estimated in a meta-analysis with heterogeneity. Traditionally, meta-analysts have tried to arrive at techniques that recover the true average effect of all conducted studies (AEA – average effect of all studies). Simonsohn et al (2014) propose estimating a different magnitude; the average effect of the studies one observes, rather than of all studies (AEO – average effect of observed studies). See Simonsohn et al (2014), the associated Supplementary Material 2, and also this blog post for arguments why they think this is a more useful quantity to estimate.

Hence, an investigation of the topic can be done on two levels: A) What is the more appropriate estimand (AEA or AEO?), and B) Under what conditions are estimators able to recover the respective true value with the least bias and least variance?

Instead of updating the section of the current blog post in the light of our discussion, I decided to cut it out and to move the topic to a future blog post. Likewise, one part of our manuscript’s revision will be a more detailed discussion about excatly these differences.

I archived the previous version of the blog post here.

Ignore overadjustments in the opposite direction

Many bias-correcting methods are driven by QRPs – the more QRPs, the stronger the downward correction. However, this effect can get so strong, that methods overadjust into the opposite direction, even if all studies in the meta-analysis are of the same sign:

 

Note: You need to set the option “Keep negative estimates” to get this plot.

Our recommendation: Ignore bias-corrected results that go into the opposite direction; set the estimate to zero, do not reject H₀.

Do not take it seriously if PET-PEESE does a reverse correction

Typical small-study effects (e.g., by p-hacking or publication bias) induce a negative correlation between sample size and effect size – the smaller the sample, the larger the observed effect size. PET-PEESE aims to correct for that relationship. In the absence of bias and QRPs, however, random fluctuations can lead to a positive correlation between sample size and effect size, which leads to a PET and PEESE slope of the unintended sign. Without publication bias, this reversal of the slope actually happens quite often.

See for example the next figure. The true effect size is zero (red dot), naive random effects meta-analysis slightly overestimates the true effect (see black dotted triangle), but PET and PEESE massively overadjust towards more positive effects:

 

As far as I know, PET-PEESE is typically not intended to correct in the reverse direction. An underlying biasing process would have to systematically remove small studies that show a significant result with larger effect sizes, and keep small studies with non-significant results. In the current incentive structure of psychological research, I see no reason for such a process, unless researchers are motivated to show that a (maybe politically controversial) effect does not exist.

Our recommendation: Ignore the PET-PEESE correction if it has the wrong sign, unless there are good reasons for an untypical selection process.

 

PET-PEESE sometimes overestimates, sometimes underestimates

A bias can be more easily accepted if it always is conservative – then one could conclude: “This method might miss some true effects, but if it indicates an effect, we can be quite confident that it really exists”. Depending on the conditions (i.e., how much publication bias, how much QRPs, etc.), however, PET/PEESE sometimes shows huge overestimation and sometimes huge underestimation.

For example, with no publication bias, some heterogeneity (τ=0.2), and severe QRPs, PET/PEESE underestimates the true effect of δ = 0.5:

In contrast, if no effect exists in reality, but strong publication bias, large heterogeneity and no QRPs, PET/PEESE overestimates at lot:

In fact, the distribution of PET/PEESE estimates looks virtually identical for these two examples, although the underlying true effect is δ = 0.5 in the upper plot and δ = 0 in the lower plot. Furthermore, note the huge spread of PET/PEESE estimates (the error bars visualize the 95% quantiles of all simulated replications): Any single PET/PEESE estimate can be very far off.

Our recommendation: As one cannot know the condition of reality, it is probably safest not to use PET/PEESE at all.

 

Recommendations in a nutshell: What you should not use in a meta-analysis

Again, please consider the “Constraints on Generality” disclaimer above.

  • When you can exclude publication bias (i.e., in the context of registered reports), do not use bias-correcting techniques. Even in the presence of some QRPs they perform worse than plain random effects meta-analysis.
  • In any other setting except registered reports, expect publication bias, and do not use random effects meta-analysis or trim-and-fill. Both will give you a 100% false positive rate in typical settings, and a biased estimation.
  • Even if all studies entering a meta-analysis point into the same direction (e.g., all are positive), bias-correcting techniques sometimes overadjust and return a significant estimate of the opposite direction. Ignore these results, set the estimate to zero, do not reject H₀.
  • Sometimes PET/PEESE adjust into the wrong direction (i.e., increasing the estimated true effect size)

As with any general recommendations, there might be good reasons to ignore them.

Additional technical recommendations

  • The p-uniform package (v. 0.0.2) very rarely does not provide a lower CI. In this case, ignore the estimate.
  • Do not run p-curve or p-uniform on <=3 significant and directionally consistent studies. Although computationally possible, this gives hugely variable results, which are often very biased. See our supplemental material for more information and plots.
  • If the 3PSM method (in the implementation of McShane et al., 2016) returns an incomplete covariance matrix, ignore the result (even if a point estimate is provided).
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Two meanings of priors, part II: Quantifying uncertainty about model parameters

by Angelika Stefan & Felix Schönbrodt

This is the second part of “Two meanings of priors”. The first part explained a first meaning – “priors as subjective probabilities of models”. While the first meaning of priors refers to a global appraisal of existing hypotheses, the second meaning of priors refers to specific assumptions which are needed in the process of hypothesis building. The two kinds of priors have in common that they are both specified before concrete data are available. However, as it will hopefully become evident from the following blog post, they differ significantly from each other and should be distinguished clearly during data analysis.

The second meaning of priors: Prior distribution for parameters

In order to know how well evidence supports a hypothesis compared to another hypothesis, one must know the concrete specifications of each hypothesis. For example, in the tea tasting experiment, each hypothesis was characterized by a specific probability (e.g., the success rate of exactly 0.5 in HFisher of the previous blog post). What might sound trivial at first – deciding on the concrete specifications of a hypothesis – is in fact one of the major challenges when doing Bayesian statistics. Scientific theories are often imprecise, resulting in more than one plausible way to derive a hypothesis. With deciding upon one specific hypothesis, often new auxiliary assumptions are made. These assumptions, which are needed in order to specify a hypothesis adequately, are called “priors” as well. They influence the formulation and interpretation of the likelihood (which gives you the plausibility of data under a specific hypothesis). We will illustrate this in an example.

How much money do we spend for lunch?

Fig. 2: A typical lunch in Germany: Schweinshaxe, Brezn and 0.5 liter of beer.

A food company conducts market research in a large German city. They know from a recent representative enquiry by the German Federal Statistical Office that Germans spend on average 4.50 € for their lunch (standard deviation: 0.60 €). Now they want to know if the inhabitants of one specific city spend more money for their lunch compared to the German average. They expect lunch expenses to be especially high in this city because of the generally high living costs. In a traditional testing procedure in inferential statistics the food company would formulate two hypotheses to test their assumption: a null and an alternative hypothesis: H0: µ ≤ 4.50 and H1: µ > 4.50.

In Bayesian hypothesis testing, the formulation of the hypotheses has to be more precise than this. We need precise hypotheses as a basis for the likelihood functions which assign probability values to possible states of reality. The traditional formulation, µ > 4.50, is too vague for that purpose: Is any lunch cost above 4.50€ a priori equally likely? Is it plausible that a lunch costs 1,000,000€ on average? Probably not. Not every state of reality is, a priori, equally plausible. “Models connect theory to data“ (Rouder, Morey, & Wagenmakers, 2016), and a model that predicts everything predicts nothing.

As Bayesian statisticians we therefore must ask ourselves: Which values are more plausible given that our hypotheses are true? Of course, our knowledge differs from case to case in this point. Sometimes, we may be able to commit to a very small range of plausible values or even to a single value (in this case, we would call the respecting hypothesis a “point hypothesis”). Theories in physics sometimes predict a single state of reality: “If this theory is true, then the mass of a Chicks boson is exactly 1.52324E-16 gr”.

 

Point hypotheses in the food company example

More often, however, our knowledge about plausible values under a certain theory might be less precise, leading to a wider range of plausible values. Hence, the prior in the second sense defines the probability of a parameter value given a hypothesis, p(θ | H1).

Let us come back to the food company example. Their null hypothesis might be that there is no difference between the city in the focus of their research project and the German average. Hence, the null hypothesis predicts an average lunch cost of 4.50€. With the alternative hypothesis, it becomes slightly more complex. They assume that average lunch expenses in the city should be higher than the German average, so the most plausible value under the alternative hypothesis should be higher than 4.5. However, they may deem it very improbable that the mean lunch expenses are more than two standard deviations higher than the German average (so, for example, it should be very improbable that someone spends more than, say, 10 EUR for lunch even in the expensive city). With this knowledge, they can put most plausibility on values in a range from 4.5 to 5.7 (4.5 + 2 standard deviations). They could further specify their hypothesis by claiming that the most plausible value should be 5.1, i.e., one standard deviation higher than the German average. The elements of these verbal descriptions of the alternative hypothesis can be summarized in a truncated normal distribution that is centered over 5.1 and truncated at 4.5 (as the directional hypothesis does not predict values in the opposite direction).

 

Truncated normal distribution centered over 5.1 and truncated at 4.5

With this model specification, the researchers would place 13% of the probability mass on values larger than 2SD of the general population (i.e., > 5.7).

Making it even more complex, they could quantify their uncertainty about the most plausible value (i.e., the maximum of the density distribution) by assigning another distribution to it. For example, they could build a normal distribution around it, with a mean of 5.1 and a standard deviation of 0.3. This would imply that in their opinion, 5.1 is the “most plausible most plausible value” but that values between 4.8 and 5.4 are also potential candidates for the most plausible value.

Why making it complicated if it’s so simple in traditional hypothesis testing?

What you can notice in the example about the development of hypotheses is that the market researchers have to make auxiliary assumptions on top of their original hypothesis (which was H1: µ > 4.5). If possible, these prior plausibilities should be informed by theory or by previous empirical data. Specifying alternative hypothesis in this way may seem to be an unnecessary burden compared to traditional hypothesis testing where these extra assumptions seemingly are not necessary. Except that they are necessary. Without going into detail in this blog post, we recommend to read Rouder et al.’s (2016a) “Is there a free lunch in inference?“, with the bottom line that principled and rational inference needs specified alternative hypotheses. (For example, in Neyman-Pearson testing, you also need to specify a precise alternative hypothesis that refers to the “smallest effect size of interest”)

Furthermore, readers might object: “Researchers rarely have enough background knowledge to specify models that predict data“. Rouder et al. (2016b) argue that this critique is overstated, as (1) with proper elicitation, researchers often know much more than they initially think, (2) default models can be a starting point if really no information is available, and (3) several models can be explored without penalty.

Do the two kinds of priors depend on each other in some way?

A question that may come to your mind soon after you understood the difference between the two kinds of priors is: If they both are called “priors”, do they depend on each other in some way? Does the formulation of your “personal prior plausibility of a hypothesis” (like the skeptical observer’s prior on Mrs. Bristol’s tea tasting abilities) influence the specification of your model (like the hypothesis specification in the second example) or vice versa?

The straightforward answer to this question is “no, they don’t”. This can be easily illustrated in a case where the prior conviction of a researcher runs against the hypothesis he or she wants to test. The food company in the second example has sophisticatedly determined the likelihood of the two hypotheses (H0 and H1), which they want to pit against each other. They are probably considerably convinced that the specification of the alternative hypothesis describes reality better than the specification of the null hypothesis. In a simplified form, their prior odds (i.e., priors in the first sense) can be described as a ratio like 10:1. This would mean that they deem the alternative hypothesis ten times as likely as the null hypothesis. However, another food company, may have prior odds of 3:5 while conducting the same test (i.e., using the same prior plausibilities of model parameters). This shows that priors in the first sense are independent of priors in the second sense. Priors in the first sense change with different personal convictions while priors in the second sense remain constant. Similarly, prior beliefs can change after seeing the data – the formulation of the model (i.e., what a theory predicts) stays the same. (As long as the theory, from which the model specification is derived, does not change. In an estimation context, the model parameters are updated by the data.)

Summary of both blog posts

The term “prior” has two meanings in the context of Bayesian hypothesis testing. The first one, usually applied in Bayes factor analysis, is equivalent to a prior subjective probability of a hypothesis (“how plausible do you deem a hypothesis compared to another hypothesis before seeing the data”). The second meaning refers to the assumptions made in the specification of the model of the hypotheses which are needed to derive the likelihood function. These two meanings of the term “prior” have to be distinguished clearly during data analysis, especially as they do not depend on each other in any way. Some researchers (e.g., Dienes, 2016) therefore suggest to call only priors in the first sense “priors” and speak about “specification of the model” when referring to the second meaning.

Read the first part of this blog post: Priors as the plausibility of models

References

Dienes, Z. (2011). Bayesian versus orthodox statistics: Which side are you on?. Perspectives On Psychological Science, 6(3), 274-290. http://doi:10.1177/1745691611406920

Dienes, Z. (2016). How Bayes factors change scientific practice. Journal Of Mathematical Psychology, 7278-89. http://doi:10.1016/j.jmp.2015.10.003

Lindley, D. V. (1993). The analysis of experimental data: The appreciation of tea and wine. Teaching Statistics, 15(1), 22-25. http://dx.doi.org/10.1111/j.1467-9639.1993.tb00252.x

Rouder, J. N., Morey, R. D., Verhagen, J., Province, J. M., & Wagenmakers, E. J. (2016a). Is there a free lunch in inference? Topics in Cognitive Science, 8, 520–547. http://doi.org/10.1111/tops.12214

Rouder, J. N., Morey, R. D., & Wagenmakers, E. J. (2016b). The Interplay between Subjectivity, Statistical Practice, and Psychological Science. Collabra, 2(1), 6–12. http://doi.org/10.1525/collabra.28

library(truncnorm)

# parameters for H1 model
M <- 5.1
SD <- 0.5

range <- seq(4.5, 7, by=.01)
plausibility <- dtruncnorm(range, a=4.5, b=Inf, mean=M, sd=SD)
plot(range, plausibility, type="l", xlim=c(4, 7), axes=FALSE, ylab="Plausibility", xlab="Lunch cost in €", mgp = c(2.5, 1, 0))
axis(1)

# Get the axis ranges, draw arrow
u <- par("usr")
points(u[1], u[4], pch=17, xpd = TRUE)
lines(c(u[1], u[1]), c(u[3], u[4]), xpd = TRUE)

abline(v=4.5, lty="dotted")

# What is the probability of values > 5.7?
1-ptruncnorm(5.7, a=4.5, b=Inf, mean=M, sd=SD)
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Two meanings of priors, part I: The plausibility of models

by Angelika Stefan & Felix Schönbrodt

When reading about Bayesian statistics, you regularly come across terms like “objective priors“, “prior odds”, “prior distribution”, and “normal prior”. However, it may not be intuitively clear that the meaning of “prior” differs in these terms. In fact, there are two meanings of “prior” in the context of Bayesian statistics: (a) prior plausibilities of models, and (b) the quantification of uncertainty about model parameters. As this often leads to confusion for novices in Bayesian statistics, we want to explain these two meanings of priors in the next two blog posts*. The current blog post covers the the first meaning of priors (link to part II).

The first meaning of “prior”: Prior plausibility for models

In this context, the term “prior” incorporates the personal assumptions of a researcher on the probability of a hypothesis (p(H1)) relative to a competing hypothesis, which has the probability p(H2). Hence, the meaning of this prior is “how plausible do you deem a model relative to another model before looking at your data”. The ratio of the two priors of the models, that is “how probable do you consider H1 compared to H2”, is called “prior odds”: p(H1) / p(H2).

The first meaning of priors is used in the context of Bayes factor analysis, where you compare two different hypotheses. In Bayes factor analysis, prior odds are updated by the likelihood ratio of the two hypotheses, which contains the information from the data, and result in the posterior odds (“what you believe after looking at your data”):

\large \frac{Posterior_{H1}}{Posterior_{H2}} =\frac{Likelihood_{H1}}{Likelihood_{H2}}\cdot\frac{Prior_{H1}}{Prior_{H2}}

The prior belief is called “subjective”, but this label does not imply that it is “arbitrary”, “unprincipled”, or “irrational”. In contrast, the prior belief can (and preferably should) be informed by previous data or experiences. For example, it can be a belief that started with an equipoise (50/50) position, but has been repeatedly updated by data. But within the bounds of rationality and consistency, people still can come to considerably different prior beliefs, and might have good arguments for their choice – that’s why it is called “subjective”. But initially differing degrees of belief will converge as more and more evidence comes in. We will observe this in the following example.

The incredible tea tasting abilities of Lady Muriel Bristol

Fig. 1: Muriel Bristol surprising her husband (William) by making the correct guess for the fifth time in a row (reenacted scene)

The classical experiment of tasting tea has already been described in the context of Bayesian hypothesis testing by Lindley (1993). We will present a simpler form here. Dr. Muriel Bristol, a scientist working in the field of alga biology who was acquainted to the statistician R. A. Fisher, claimed that she could discriminate whether milk is put in a cup before or after the tea infusion during the process of preparing tea with milk. However, Mr. Fisher considered this very unlikely.

So they decided to run an experiment: Muriel Bristol tasted several cups of tea in a row, making a guess on the preparation procedure for each cup. Unlike in the original story, where inferential statistics were consulted to solve the disagreement, we will employ Bayesian statistics to track how prior convictions in this example change. If Muriel Bristol makes her guesses only based on chance as Mr. Fisher supposes, she has a probability of success of 50% in each trial. Before observing her performance, Mr. Fisher should therefore consider it very likely that she is right about the procedure in about 50% of the trials across all trials. We can therefore assume a point hypothesis: HFisher: Success rate (SR) = 0.5. Muriel Bristol, on the other hand, is very confident in her divination skills. She claims to get 80% of trials correct, which can be equally captured in a point hypothesis: HMuriel: SR = 0.8.

Two observers with different prior beliefs

To introduce prior beliefs about hypotheses and show how they change with upcoming evidence, we want to introduce two additional persons. The first one is a slightly skeptical observer who tends to favor HFisher, but does not completely rule out that Mrs. Bristol could be right with her hypothesis. More formally, we could describe this position as: P(HFisher) = 0.6 and P(HMuriel) = 0.4. This means that his prior odds are P(HFisher)/P(HMuriel) = 3:2. Fisher’s hypothesis is 1.5 times more likely to him than Muriel Bristol’s hypothesis.

The second additional person we would like to introduce is William, Muriel Bristol’s loving husband who fervently advocates her position. He knows his wife would never (or at least very rarely) make wrong claims, concerning tea preparation and all others issues of their marriage. He therefore assigns a much higher subjective probability to her hypothesis (P(HMuriel) = 0.9) than to the one of Mr. Fisher (P(HFisher) = 0.1). His prior odds are therefore P(HFisher)/P(HMuriel) = 1:9. Please note that the content of the hypotheses (the proposed success rates 0.5 and 0.8, which are the parameters of the model) is logically independent of the probability of the hypotheses (priors) that our two observers have.

How to update prior beliefs about hypotheses

During the process of hypothesis testing, these two priors are updated with the existing evidence. It is reported that Muriel Bristol’s performance at the experiment was extraordinarily good. We therefore assume that out of the first 6 trials of the experiment she got 5 correct.

With this information, we can now compute the likelihood of the data under each of the hypotheses (for more information on the computation of likelihoods, see Alexander Etz’s blog:

L_{Fisher} = 0.5^5 \cdot 0.5^1 =  0.016

L_{Muriel} = 0.8^5 \cdot 0.2^1 = 0.066

The computation of the likelihoods does not involve the prior model probabilities of our observers. What can be seen is that the data are more likely under Muriel Bristol’s hypothesis than under Mr. Fisher’s. This should not come as a surprise as Muriel Bristol claimed that she could make a very high percentage of right guesses and the data show a very high percentage of right guesses whereas Mr. Fisher assumed a much lower percentage of right guesses. To emphasize this difference in likelihoods and to assign it a numerical value, we can compute the likelihood ratio (Bayes factor):

BF_{FM} = LR_{FM} = \frac{L_{Fisher}}{L_{Muriel}} = 0.016 / 0.066 = 0.238

BF_{MF} = LR_{MF} = \frac{L_{Muriel}}{L_{Fisher}} = 0.066 / 0.016 = 4.19

This ratio means that the data are 4.19 times as likely under Mrs. Bristol’s hypothesis as under Mr. Fisher’s hypothesis. It does not matter how you order the likelihoods in the fraction, the meaning remains constant (see this blog post).

How does this likelihood change the prior odds of both our slightly skeptical observer and William Bristol? Bayes theorem shows that prior odds can be updated by multiplying them with the likelihood ratio (Bayes factor):

\frac{ Posterior(H_{Fisher}) } {Posterior(H_{Muriel})} =  \frac{Prior(H_{Fisher})}{Prior(H_{Muriel})} \cdot \text {Bayes Factor}

First, we will focus on the posterior odds of the slightly skeptical observer. To remember, the slightly skeptical observer had assigned a probability of 0.6 to Mr. Fisher’s hypothesis and a probability of 0.4 to Muriel Bristol’s hypothesis before seeing the data, which resulted in prior odds of 3:2 for Mr. Fisher’s hypothesis. How do these convictions change now when the experiment has conducted? To examine this, we simply have to insert all known values in the equation:

\frac {3}{2} \cdot 0.238 = 0.357 = \frac {1}{2.8}

This shows that the prior odds of the slightly skeptical observer changed from 3:2 to posterior odds of 1:2.8. This means that whereas before the experiment the slightly skeptical observer had deemed Mr. Fisher’s hypothesis more plausible than Mrs. Bristol’s hypothesis, he changed his opinion after seeing the data, now preferring Mrs. Bristol’s hypothesis over Mr. Fisher’s.

The same equation can be applied to William Bristol’s prior odds:

\frac {1}{9} \cdot 0.238 = 0.0264 = \frac {1}{37.9}

What we can notice is that after taking the data into consideration both prior odds display a higher amount of agreement with Muriel Bristol and reduced confidence in Mr. Fisher’s hypothesis. Whereas the convictions of the slightly skeptical observer were changed in favor of Muriel Bristol’s hypothesis after the experiment, William Bristol’s prior convictions were strengthened.

Something else you can notice is that compared to William Bristol the slightly skeptical observer still assigns a higher plausibility to Mr. Fisher’s hypothesis. This rank order between the two priors will remain no matter what the data look like. Even if Muriel Bristol made, say, 100/100 correct guesses, the slightly skeptical observer would trust less in her hypothesis than her husband. However, with increasing evidence the absolute difference between both observers will decrease more and more.

Summary

This blog post explained the first meaning of “prior” in the context of Bayesian statistics. It can be defined as the subjective plausibility a researcher assigns to a hypothesis compared to another hypothesis before seeing the data. As illustrated in the tea-tasting example, these prior beliefs are updated with upcoming evidence in the research process. In the next blog post, we will explain a second meaning of “priors”: The quantification of uncertainty about model parameters.

Continue reading part II: Quantifying uncertainty about model parameters

We want to thank Eric-Jan Wagenmakers for helpful comments on a previous version of the post.

*As a note: Both meanings in fact can be unified, but for didactic purpose we think it makes sense to keep them distinct as a start.

References

Dienes, Z. (2011). Bayesian versus orthodox statistics: Which side are you on?. Perspectives On Psychological Science, 6(3), 274-290. http://doi:10.1177/1745691611406920

Dienes, Z. (2016). How Bayes factors change scientific practice. Journal Of Mathematical Psychology, 7278-89. http://doi:10.1016/j.jmp.2015.10.003

Lindley, D. V. (1993). The analysis of experimental data: The appreciation of tea and wine. Teaching Statistics, 15(1), 22-25. http://dx.doi.org/10.1111/j.1467-9639.1993.tb00252.x

Rouder, J. N., Morey, R. D., Verhagen, J., Province, J. M., & Wagenmakers, E. J. (2016a). Is there a free lunch in inference? Topics in Cognitive Science, 8, 520–547. http://doi.org/10.1111/tops.12214

Rouder, J. N., Morey, R. D., & Wagenmakers, E. J. (2016b). The Interplay between Subjectivity, Statistical Practice, and Psychological Science. Collabra, 2(1), 6–12. http://doi.org/10.1525/collabra.28

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