Replication studies are often regarded as the means to scrutinize scientific claims of prior studies. They are also at the origin of the scientific debate on what has been labeled “replication crisis.” The fact that the results of many studies cannot be “replicated” in subsequent investigations is seen as casting serious doubts on the quality of empirical research. Unfortunately, the interpretation of replication studies is itself plagued by two intimately linked problems: first, the conceptual background of different types of replication often remains unclear. Second, inductive inference often follows the rationale of conventional significance testing with its misleading dichotomization of results as being either “significant” (positive) or “not significant” (negative). A poor understanding of inductive inference, in general, and the p-value, in particular, will cause inferential errors in all studies, be they initial ones or replication studies.
Amalgamating taxonomic proposals from various sources, we believe that it is useful to distinguish three types of replication studies:
1. Pure replication is the most trivial of all replication exercises. It denotes a subsequent “study” that is limited to verifying computational correctness. It therefore uses the same data (sample) and the same statistical model as the initial study.
2. Statistical replication (or reproduction) applies the same statistical model as used in the initial study to another random sample of the same population. It is concerned with the random sampling error and statistical inference (generalization from a random sample to its population). Statistical replication is the very concept upon which frequentist statistics and therefore the p-value are based.
3. Scientific replication comprises two types of robustness checks: (i) The first one uses a different statistical model to reanalyze the same sample as the initial study (and sometimes also another random sample of the same population). (ii) The other one extends the perspective beyond the initial population and uses the same statistical model for analyzing a sample from a different population.
Statistical replication is probably the most immediate and most frequent association evoked by the term “replication crisis.” It is also the focus of this blog in which we illustrate that re-finding or not re-finding “statistical significance” in statistical replication studies does not tell us whether we fail to replicate a prior scientific claim or not.
In the wake of the 2016 ASA-statement on p-values, many economists realized that p-values and dichotomous significance declarations do not provide a clear rationale for statistical inference. Nonetheless, many economists seem still to be reluctant to renounce dichotomous yes/no interpretations; and even those who realize that the p-value is but a graded measure of the strength of evidence against the null are often not fully aware that an informed inferential interpretation of the p-value requires considering its sample-to-sample variability.
We use two simulations to illustrate how misleading it is to neglect the p-value’s sample-to-sample variability and to evaluate replication results based on the positive/negative dichotomy. In each simulation, we generated 10,000 random samples (statistical replications) based on the linear “reality” y = 1 + βx + e, with β = 0.2. The two realities differ in their error terms: e~N(0;3), and e~N(0;5). Sample size is n = 50, with x varying from 0.5 to 25 in equal steps of 0.5. For both the σ = 3 and σ = 5 cases, we ran OLS-regressions for each of the 10,000 replications, which we then ordered from the smallest to the largest p-value.
Table 1 shows selected p-values and their cumulative distribution F(p) together with the associated coefficient estimates b and standard error estimates s.e. (and their corresponding Z scores under the null).The last column displays the power estimates based on the naïve assumption that the coefficient b and the standard error s.e. that we happened to estimate in the respective sample were true.
Table 1: p-values and associated coefficients and power estimates for five out of 10,000 samples (n = 50 each)†
Our simulations illustrate one of the most essential features of statistical estimation procedures, namely that our best unbiased estimators estimate correctly on average. We would therefore need all estimates from frequent replications – irrespective of their p-values and their being large or small – to obtain a good idea of the population effect size. While this fact should be generally known, it seems that many researchers, cajoled by statistical significance language, have lost sight of it. Unfortunately, this cognitive blindness does not seem to stop short of those who, insinuating that replication implies a reproduction of statistical significance, lament that many scientific findings cannot be replicated. Rather, one should realize that each well-done replication adds an additional piece of knowledge. The very dichotomy of the question whether a finding can bereplicated or not, is therefore grossly misleading.
Contradicting many neat, plausible, and wrong conventional beliefs, the following messages can be learned from our simulation-based statistical replication exercise:
1. While conventional notation abstains from advertising that the p-value is but a summary statistic of a noisy random sample, the p-value’s variability over statistical replications can be of considerable magnitude. This is paralleled by the variability of estimated coefficients. We may easily find a large coefficient in one random sample and a small one in another.
2. Besides a single study’s p-value, its variability –and, in dichotomous significance testing, the statistical power (i.e., the zeroth order lower partial moment of the p-value distribution at 0.05) – determines the repeatability in statistical replication studies. One needs an assumption regarding the true effect size to assess the p-value’s variability. Unfortunately, economists often lack information regarding the effect size prior to their own study.
3. If we rashly claimed a coefficient estimated in a single study to be true, we would not have to be surprised at all if it cannot be “replicated” in terms of re-finding statistical significance. For example, if an effect size and standard error estimate associated with a p-value of 0.05 were real, we would necessarily have a mere 50% probability (statistical power) of finding a statistically significant effect in replications in a one-sided test.
4. Low p-values do not indicate results that are more trustworthy than others. Under reasonable sample sizes and population effect sizes, it is the abnormally large sample effect sizes that produce “highly significant” p-values. Consequently, even in the case of a highly significant result, we cannot make a direct inference regarding the true effect. And by averaging over “significant” replications only, we would necessarily overestimate the effect size because we would right-truncate the distribution of the p-value which, in turn, implies a left-truncation of the distribution of the coefficient over replications.
5. In a single study, we have no way of identifying the p-value below which (above which) we overestimate (underestimate) the effect size. In the σ = 3 case, a p-value of 0.001 was associated with a coefficient estimate of 0.174 (underestimation). In the σ = 5 case, it was linked to a coefficient estimate of 0.304 (overestimation).
6. Assessing the replicability (trustworthiness) of a finding by contrasting the tallies of “positive” and “negative” results in replication studies has long been deplored as a serious fallacy (“vote counting”) in meta-analysis. Proper meta-analysis shows that finding non-significant but same-sign effects in a large number of replication studies may represent overwhelming evidence for an effect. Immediate intuition for this is provided when looking at confidence intervals instead of p-values. Nonetheless, vote counting seems frequently to cause biased perceptions of what is a “replication failure.”
Prof. Norbert Hirschauer, Dr. Sven Grüner, and Prof. Oliver Mußhoff are agricultural economists in Halle (Saale) and Göttingen, Germany. Prof. Claudia Becker is an economic statistician in Halle (Saale). The authors are interested in connecting with economists who have an interest to further concrete steps that help prevent inferential errors associated with conventional significance declaration in econometric studies. Correspondence regarding this blog should be directed to Prof. Hischauer at firstname.lastname@example.org.
[From the article “Data Access, Transparency, and Replication: New Insights from the Political Behavior Literature” by Daniel Stockemer, Sebastian Koehler, and Tobias Lentz, in the October issue of PS: Political Science & Politics]
“How many authors of articles published in journal with no mandatory data-access policy make their dataset and analytical code publicly available? If they do, how many times can we replicate the results? If we can replicate them, do we obtain the same results as reported in the respective article?”
“We answer these questions based on all quantitative articles published in 2015 in three behavioral journals—Electoral Studies, Party Politics, and Journal of Elections, Public Opinion & Parties—none of which has any binding data-access or replication policy as of 2015. We found that few researchers make their data accessible online and only slightly more than half of contacted authors sent their data on request. Our results further indicate that for those who make their data available, the replication confirms the results (other than minor differences) reported in the initial article in roughly 70% of cases.”
“However, more concerning, we found that in 5% of articles, the replication results are fundamentally different from those presented in the article. Moreover, in 25% of cases, replication is impossible due to poor organization of the data and/or code.”
To read more, click here. (NOTE: article is behind a paywall.)
[From the abstract of the working paper, “US Courts of Appeal cases frequently misinterpret p-values and statistical significance: An empirical study”, by Adrian Barnett and Steve Goodman, posted at Open Science Framework]
“We examine how p-values and statistical significance have been interpreted in US Courts of Appeal cases from 2007 to 2017. The two most common errors were: 1) Assuming a “non-significant” p-value meant there was no important difference and the evidence could be discarded, and 2) Assuming a “significant” p-value meant the difference was important, with no discussion of context or practical significance. The estimated mean probability of a correct interpretation was 0.21 with a 95% credible interval of 0.11 to 0.31.”
In a recent editorial in Management Review Quarterly, the journal invited replications, and put forth the following “Seven Principles of Effective Replication Studies”:
#1. “Understand that replication is not reproduction”
#2. “Aim to replicate published studies that are relevant”
#3. “Try to replicate in a way that potentially enhances the generalizability of the original study”
#4. “Do not compromise on the quality of data and measures”
#5. “Nonsignificant findings are publishable but need explanation”
#6. “Extensions are possible but not necessary”
#7. “Choose an appropriate format based on the replication approach”
We have to admit that #5 caught our eye. Here is the explanation the editors gave:
“…nonsignificant results or ‘failed’ replications can be extremely important to further theory development. However, they need more information and explanation than ‘successful’ replications. Replication studies should account for this and include detailed comparison tables of the original and replicated results and an elaborate discussion of the differences and similarities between the studies. Authors need to make an effort to explain deviant findings. The differences might be due to different contextual environments from where the sample is drawn; the use of different, more appropriate measures; different statistical methods or simply a result of frequentist null-hypothesis testing where, by definition, false positives are possible (Kerr, 1998). In any case, authors should comment on these possibilities and take a clear stand.”
Note that a replicating author who can’t explain why their replication did not reproduce the original study’s results is given the following lifeline: “The differences might be due to… simply a result of frequentist null-hypothesis testing where, by definition, false positives are possible.”
Is it reasonable that nonsignificant results in replications — and we acknowledge that the fact it is a replication is important — be held to a higher standard of justification than significant results?
We think so, but we wonder if it really will be sufficient for MRQ for a replicating author to “explain” their nonsignificant results by claiming the original study was a 5%, rare result of samping error.
[From the article “More and more scientists are preregistering their studies. Should you?” by Kai Kupferschmidt, published in Science]
“…Preregistration, in its simplest form, is a one-page document answering basic questions such as: What question will be studied? What is the hypothesis? What data will be collected, and how will they be analyzed? In its most rigorous form, a “registered report,” researchers write an entire paper, minus the results and discussion, and submit it for peer review at a journal, which decides whether to accept it in principle. After the work is completed, reviewers simply check whether the researchers stuck to their own recipe; if so, the paper is published, regardless of what the data show.”
“…Several databases today host preregistrations. The Open Science Framework, run by COS, is the largest one; it has received 18,000 preregistrations since its launch in 2012, and the number is roughly doubling every year. The neuroscience journal Cortex, where Chambers is an editor, became the first journal to offer registered reports in 2013; it has accepted 64 so far, and has published results for 12. More than 120 other journals now offer registered reports, in fields as diverse as cancer research, political science, and ecology.
“…Still, the model is not attractive to everyone. Many journals are afraid of having to publish negative results, Chambers says. And some researchers may not want to commit to publishing whatever they find, regardless of whether it supports a hypothesis.”
“….There are other drawbacks…”
“…It’s not easy to tell how real preregistration’s potential benefits and drawbacks are. Anne Scheel of the Eindhoven University of Technology in the Netherlands, for instance, recently set out to answer a seemingly simple question: Do registered reports lead to more negative results being published? “I’m quite shocked how hard it is,” says Scheel.”
“…For preregistration to be a success, the protocols need to be short, simple to write, and easy to read, Simmons says. That’s why in 2015 he, Nelson, and Simonsohn launched a website, aspredicted.org, that gives researchers a simple template for generating a preregistration.”
[From the abstract of the forthcoming paper, “Replication studies in economics—How many and which papers are chosen for replication, and why?” by Frank Mueller-Langer, Benedikt Fecher, Dietmar Harhoff, and Gert G. Wagner, forthcoming in the journal, Research Policy]
“We investigate how often replication studies are published in empirical economics and what types of journal articles are replicated. We find that between 1974 and 2014 0.1% of publications in the top 50 economics journals were replication studies. We consider the results of published formal replication studies (whether they are negating or reinforcing) and their extent: Narrow replication studies are typically devoted to mere replication of prior work, while scientific replication studies provide a broader analysis. We find evidence that higher-impact articles and articles by authors from leading institutions are more likely to be replicated, whereas the replication probability is lower for articles that appeared in top 5 economics journals. Our analysis also suggests that mandatory data disclosure policies may have a positive effect on the incidence of replication.”
To access the article, click here (but note that it is behind a paywall).
[From the article “The changing forms and expectations of peer review” by Serge Horbach and Willem Halffman, published in Research Integrity and Peer Review, 2018, 3:8]
This is a wonderful article that provides a comprehensive discussion of peer review in the context of scientific quality and integrity. Here are some highlights from the article.
– Provides context for arguments around the role of peer review in ensuring scientific quality/integrity. This includes references from those arguing that it performs that function adequately, to others that argue it fails miserably.
– It discusses the historical evolution of peer-review, arguing that it did not become a mainstream journal practice until after the Second World War.
– Explains how the desire to ensure fairness and objectivity led to single-blind, double-blind, and triple-blind reviewing (where even the handling editor does not know the identify of the author). See table below:
– Discusses the evidence for bias (particularly gender and institutional-affiliation bias) in peer review.
– It is interesting that the same concern for reviewer bias has led to diametrically opposite forms of peer review: double-blind peer review and open peer review.
– With the advent of extra-journal publication outlets, such as pre-print archives, there has been discussion that peer review should serve less the role of quality assurance, and more the goal of providing context and connection to existing literature.
– Makes the argument that one of the motivations behind “registered reports”, where journals decide to publish a paper based on its research design — independently of its results — is that this would provide a greater incentive to undertake replications.
– Related to the replication crisis and publication bias, peer review at some journals has moved to re-focussing assessment away from novelty and statistical significance, and towards importance of the research question and soundness of research design.
– Another development in peer review has been the creation of software to assist journals and reviewers in identifying plagiarism and to detect statistical errors and irregularities.
– Artificial intelligence is being looked to in order to address the burdensome task of reviewing ever-increasing numbers of scientific manuscripts. The following quote offers an intriguing look at a possible, AI future of peer review: “Chedwich deVoss, the director of StatReviewer, even claims: ‘In the not-too-distant future, these budding technologies will blossom into extremely powerful tools that will make many of the things we struggle with today seem trivial. In the future, software will be able to complete subject-oriented review of manuscripts. […] this would enable a fully automated publishing process – including the decision to publish.’”
– Given the increasingly important role that statistics play in scientific research, there is an incipient movement for journals to employ statistical experts to review manuscripts, including the contracting of reviewing to commercial providers.
– Post-publication review, such as that offered by PubPeer, has also expanded peer review outside the decision to publish research.
– Another movement in peer review has been to introduce interactive discussion between the reviewer, the author, and external “peers” before the editor makes their decision. Though this is not mentioned in the article, this is the model of peer review in place at the journal Economics: The Open Access, Open Assessment E-journal.
– The article concludes the discussion by noting that as academic publishing has become big business, with high submission and subscription fees charged to authors and readers, there is an increasing sense that academic publishers should be held responsible for the quality of their product. This has — and will have even more so in the future — consequences for peer review.
The Journal of Experimental Political Science (JEPS) just announced that is opening up a new kind of manuscript submission based on preregistered reports. Here is how they describe it:
“A preregistered report is like any other research paper in many respects. It offers a specific research question, summarizes the scholarly conversation in which the question is embedded, explicates the theoretically grounded hypotheses that offer a partial answer to the research question, and details the research design for testing the proposed hypotheses. It differs from most research papers in that a preregistered report stops here. The researchers do not take the next step of reporting results from the data they collected. Instead, they preregister the design in a third-party archive, such as the Open Science Framework, before collecting data.”
“At JEPS, we will send out preregistered reports for a review, just like other manuscripts, but we will ask reviewers to focus on whether the research question, theory, and design are sound. If the researchers carried out the proposed research a) would they make a contribution and b) would their proposed test do the job? If the answer is yes (potentially after a round of revisions), we will conditionally accept the paper and give the researchers a reasonable amount of time to conduct the study, write up the results, and resubmit the revised fully-fledged paper. At this point, we will seek the reviewers’ advice one more time and ask, “Did the researchers do what they said they were going to do?” If the answer is “yes,” we will publish the paper. It doesn’t matter if the research produced unexpected results, null findings, or inconsistent findings. In fact, we will specifically instruct reviewers at the second stage to ignore statistical significance and whether they support the authors’ hypotheses when evaluating the paper.”
This follows the recent announcement at another Cambridge University Press journal, the Japanese Journal of Political Science, that it is introducing results-free peer review (RFPR).
To read more about the JEPS announcement, click here.
[From the article, “This Cornell Food Researcher Has Had 13 Papers Retracted. How Were They Published in the First Place?” by Kiera Butler, published in Mother Jones]
“In 2015, I wrote a profile of Brian Wansink, a Cornell University behavioral science researcher who seemed to have it all: a high-profile lab at an elite university, more than 200 scientific studies to his name, a high-up government appointment, and a best-selling book.”
“…In January 2017, a team of researchers reviewed four of [Wansink’s] published papers and turned up 150 inconsistencies. Since then, in a slowly unfolding scandal, Wansink’s data, methods, and integrity have been publicly called into question. Last week, the Journal of the American Medical Association (JAMA) retracted six articles he co-authored. To date, a whopping 13 Wansink studies have been retracted.”
“… when I first learned of the criticisms of his work, I chalked it up to academic infighting and expected the storm to blow over. But as the scandal snowballed, the seriousness of the problems grew impossible to ignore. I began to feel foolish for having called attention to science that, however fun and interesting, has turned out to be so thin. Were there warning signs I missed? Maybe. But I wasn’t alone. Wansink’s work has been featured in countless major news outlets—the New York Times has called it “brilliantly mischievous.” And when Wansink was named head of the USDA in 2007, the popular nutrition writer Marion Nestle deemed it a “brilliant appointment.””
“Scientists bought it as well. Wansink’s studies made it through peer review hundreds of times—often at journals that are considered some of the most prestigious and rigorous in their fields. The federal government didn’t look too closely, either: The USDA based its 2010 dietary guidelines, in part, on Wansink’s work. So how did this happen?”
Replication seems a sensible way to assess whether a scientific result is right. The intuition is clear: if a result is right, you should get a significant result when repeating the work; if it it’s wrong, the result should be non-significant. I test this intuition across a range of conditions using simulation. For exact replications, the intuition is dead on, but when replicas diverge from the original studies, error rates increase rapidly. Even for the exact case, false negative rates are high for small effects unless the samples are large. These results bode ill for large, systematic replication efforts, which typically prioritize uniformity over fidelity and limit sample sizes to run lots of studies at reasonable cost.
The basic replication rationale goes something like this: (1) many published papers are wrong; (2) this is a serious problem the community must fix; and (3) systematic replication is an effective solution. (In recent months, I’ve seen an uptick in pre-registration as another solution. That’s a topic for another day.) In this post, I focus on the third point and ask: viewed as a statistical test, how well does systematic replication work; how well does it tell the difference between valid and invalid results?
I consider a basic replication scheme in which each original study is repeated once. This is like RPP and EERP, but unlike Many Lab as published which repeated each study 36 times and SSRP which used a two-stage replication strategy. I imagine that the replicators are trying to closely match the original study (direct replication) while doing the replications in a uniform fashion for cost and logistical reasons.
My test for replication success is the same as SSRP (what they call the statistical significance criterion): a replication succeeds if the replica has a significant effect in the same direction as the original.
A replication is exact if the two studies are sampling the same population. This is an obvious replication scenario. You have a study you think may be wrong; to check it out, you repeat the study, taking care to ensure that the replica closely matches the original. Think cold fusion. A replication is near-exact if the populations differ slightly. This is probably what systematic replication achieves, since the need for uniformity reduces precision.
Significance testing of the replica (more precisely, the statistical significance criterion) works as expected for exact replications, but error rates increase rapidly as the populations diverge. This isn’t surprising when you think about it: we’re using the replica to draw inferences about the original study; it stands to reason this will only work if the two studies are very similar.
Under conditions that may be typical in systematic replication projects, the rate of false positive mistakes calculated in this post ranges from 1-71% and false negative mistakes from 0-85%. This enormous range results from the cumulative effect of multiple unknown, hard-to-estimate parameters.
My results suggest that we should adjust our expectations for systematic replication projects. These projects may make a lot of mistakes; we should take their replication failure rates with a grain of salt.
The software supporting this post is open source and freely available in GitHub.
The software simulates studies across a range of conditions, combines pairs of studies into pairwise replications, calculates which replications pass the test, and finally computes false positive and false negative rates for conditions of interest.
The studies are simple two group comparisons parameterized by sample size and population effect size dpop (dpop≥ 0). For each study, I generate two groups of n random numbers. One group comes from a standard normal distribution with mean = 0; the other is standard normal with mean = dpop. I then calculate the p-value from a t-test. When I need to be pedantic, I use the term study set for the ensemble of studies for a given combination of n and dpop.
The program varies n from 20 to 500 and dpop from 0 to 1 with 11 discrete values each (a total of 112 = 121 combinations). It simulates 104 studies for each combination yielding about 1.2 million simulated studies. An important limitation is that all population effect sizes are equally likely within the range studied. I don’t consider publication bias which may make smaller effect sizes more likely, or any prior knowledge of expected effect sizes.
To generate pairwise replications, I consider all (ordered) pairs of study sets. For each pair, the software permutes the studies of each set, then combines the studies row-by-row. This multiplies out to 1212 = 14,641 pairs of study sets and almost 150 million simulated replications. The first study of the pair is the original and the second the replica. I consistently use the suffixes 1 and 2 to denote the original and replica respectively.
Four variables parameterize each pairwise replication: n1, n2, d1pop, and d2pop. These are the sample and population effect sizes for the two studies.
After forming the pairwise replications, the program discards replications for which the original study isn’t significant. This reflects the standard practice that non-significant findings aren’t published and thus aren’t candidates for systematic replication.
Next the program determines which replications should pass the replication test and which do pass the test. The ones that should pass are ones where the original study is a true positive, i.e., d1pop ≠ 0. The ones that do pass are ones where the replica has a significant p-value and effect size in the same direction as the original.
A false positive replication is one where the original study is a false positive (d1pop = 0) yet the replication passes the test. A false negative replication is one where the original study is a true positive (d1pop ≠ 0), yet the replication fails the test. The program calculates false positive and false negative rates (abbr. FPR and FNR) relative to the number of replications in which the original study is significant.
My definition of which replications should pass depends only on the original study. A replication in which the original study is a false positive and the replica study a true positive counts as a false positive replication. This makes sense if the overarching goal is to validate the original study. If the goal were to test the result of the original study rather than the study itself, it would make sense to count this case as correct.
To get “mistake rates” I need one more parameter: , the proportion of replications that are true. This is the issue raised in Ioannidis’s famous paper, “Why most published research findings are false” and many other papers and blog posts including one by me. The terminology for “mistake rates” varies by author. I use terminology adapted from Jager and Leek. The replication-wise false positive rate (RWFPR) is the fraction of positive results that are false positives; the replication-wise false negative rate (RWFNR) is the fraction of negative results that are false negatives.
A replication is exact if the two studies are sampling the same population; this means d1pop = d2pop.
Figure 1 shows FPR for n1 = 20 and n2 varying from 50 to 500. The x-axis shows all four parameters using d1, d2 as shorthand for d1pop, d2pop. d1pop = d2pop = 0 throughout because this is the only way to get false positives with exact replications. Figure 2 shows FNR for the same values of n1 and n2 but with d1pop = d2pop ranging from 0.1 to 1.
I mark the conventionally accepted thresholds for false positive and negative error rates (0.05 and 0.2, resp.) as known landmarks to help interpret the results. I do not claim these are the right thresholds for replications.
For this ideal case, replication works exactly as intuition predicts. FPR is the significance level divided by 2 (the factor of 2 because the effect sizes must have the same direction). Theory tell us that FNR = 1 – power and though not obvious from the graph, the simulated data agrees well.
As one would expect, if the population effect size is small, n2 must be large to reliably yield a positive result. For d = 0.2, n2 must be almost 400 in theory and 442 in the simulation to achieve FNR = 0.2; to hit FNR = 0.05, n2 must be more than 650 (in theory). These seem like big numbers for a systematic replication project that needs to run many studies.
Near exact replications
A replication is near-exact if the populations differ slightly, which means d1pop and d2pop differ by a small amount, near; technically, abs(d1pop – d2pop) ≤ near.
I don’t know what value of near is reasonable for a systematic replication project. I imagine it varies by research area depending on the technical difficulty of the experiments and the variability of the phenomena. The range 0.1-0.3 feels reasonable. I extend the range by 0.1 on each end just to be safe.
Figure 3 uses the same values of n1, n2, and d1pop as Figure 1, namely n1 = 20, n2 varies from 50 to 500, and d1pop = 0. Figure 4 uses the same values of n1 and n2 as Figure 2 but fixes d1pop = 0.5, a medium effect size. In both figures, d2pop ranges from d1pop – near to d1pop + near with values less than 0 or greater than 1 discarded. I restrict values to the interval [0,1] because that’s the range of d in the simulation.
FPR is fine when n2 is small, esp. when near is also small, but gets worse as n2 (and near) increase. It may seem odd that the error rate increases as the sample size increases. What’s going on is a consequence of power. More power is usually good, but in this setting every positive is a false positive, so more power is bad. This odd result is a consequence of how I define correctness. When the original study is a false positive (d1pop = 0) and the replica a true positive (d2pop ≠ 0), I consider the replication to be a false positive. This makes sense if we’re trying to validate the original study. If instead we’re testing the result of the original study, it would make sense to count this case as correct.
FNR behaves in the opposite direction: bad when n2 is small and better as n2 increases.
To show the tradeoff between FPR and FNR, Figure 5 plots both error rates for near = 0.1 and near = 0.3.
For near =0.1, n2 = 150 is a sweet spot with both error rates about 0.05. For near = 0.3, the crossover point is n2 = 137 with error rates of about 0.15.
FNR also depends on d1pop for “true” cases, i.e., when the original study is a true positive, getting worse when d1pop is smaller and better when d1pop is bigger. The table below shows the error rates for a few values of n2, near, and d1pop. Note that FPR only depends on n2 and near, while FNR depends on all three parameters. The FNR columns are for different values of d1popin true cases.
FNR is great for d1pop = 0.8, mostly fine for d1pop = 0.5, and bad for d1pop = 0.2. Pushing up n2 helps but even when n2 = 450, FNR is probably unacceptable for d1pop = 0.2. Increasing n2 worsens FPR. It seems the crossover point above, n2 = 137, is about right. Rounding up to 150 seems a reasonable rule-of-thumb.
Replication-wise error rates
The error rates reported so far depend on whether the original study is a false or true positive: FPR assumes the original study is a false positive, FNR assumes it’s a true positive. The next step is to convert these into replication-wise error rates: RWFPR and RWFNR. To do so, we need one more parameter: prop.true, the proportion of replications that are true.
Of course, we don’t know the value of prop.true; arguably it’s the most important parameter that systematic replication is trying to estimate. Like near , it probably varies by research field and may also depend on the quality of the investigator. Some authors assume prop.true = 0.5, but I see little evidence to support any particular value. It’s easy enough to run a range of values and see how prop.true affects the error rates.
The table below shows the results for near = 0.1, 0.3 as above, and prop.true ranging from 0.1 to 0.9. The RWFPR and RWFNR columns are for different values of d1pop in “true” cases, i.e., when the original study is a true positive.
Check out the top and bottom rows. The top row depicts a scenario where most replications are false (prop.true = 0.1) and the replicas closely match the original studies (near = 0.1); for this case, most positives are mistakes and most negatives are accurate. The bottom row is a case where most replications are true (prop.true = 0.9) and the replicas diverge from the originals (near = 0.3); here most positives are correct and, unless d1pop is large, most negatives are mistakes.
Which scenario is realistic? There are plenty of opinions but scant evidence. Your guess is as good as mine.
Systematic replication is a poor statistical test when used to validate published studies. Replication works well when care is taken to ensure the replica closely matches the original study. This is the norm in focused, one-off replication studies aiming to confirm or refute a single finding. It seems unrealistic in systematic replication projects, which typically prioritize uniformity over fidelity to run lots of studies at reasonable cost. If the studies differ, as they almost certainly must in systematic projects, mistake rates grow and may be unacceptably high under many conditions.
My conclusions depend on the definition of replication correctness, i.e., which replications should pass. The definition I use in this post depends only on the original study: a replication should pass if the original study is a true positive; the replica study is just a proxy for the original one. This makes sense if the goal is to validate the original study. If the goal were to test the result of the original study rather than the study itself, it would make sense to let true positive replicas count as true positive replications. That would greatly reduce the false positive rates I report.
My conclusions also depend on details of the simulation. An important caveat is that population effect sizes are uniformly distributed across the range studied. I don’t consider publication bias which may make smaller effect sizes more likely, or any prior knowledge of expected effect sizes. Also, in the near exact case, I assume that replica effect sizes can be smaller or larger than the original effect sizes; many investigators believe that replica effect sizes are usually smaller.
My results suggest that systematic replication is unsuitable for validating existing studies. An alternative is to switch gears and focus on generalizability. This would change the mindset of replication researchers more than the actual work. Instead of trying to refute a study, you would assume the study is correct within the limited setting of the original investigation and try to extend it to other settings. The scientific challenge would become defining good “other settings” – presumably there are many sensible choices — and selecting studies that are a good fit for each. This seems a worthy problem in its own right that would move the field forward no matter how many original studies successfully generalize.
I’ve seen plenty of bad science up close and personal, but in my experience statistics isn’t the main culprit. The big problem I see is faulty research methods. Every scientific field has accepted standard research methods. If the methods are bad, even “good” results are likely to be wrong; the results may be highly replicable but wrong nonetheless.
The quest to root out bad science is noble but ultimately futile. “Quixotic” comes to mind. Powerful economic forces shape the size and make-up of research areas. Inevitably some scientists are better researchers than others. But “Publish or Perish” demands that all scientists publish research papers. Those who can, publish good science; those who can’t, do the best they can.
We will do more good by helping good scientists do good science than by trying to slow down the bad ones. The truly noble quest is to develop tools and techniques that make good scientists more productive. That’s the best way to get more good science into the literature.
Nat Goodman is a retired computer scientist living in Seattle Washington. His working years were split between mainstream CS and bioinformatics and orthogonally between academia and industry. As a retiree, he’s working on whatever interests him, stopping from time-to-time to write papers and posts on aspects that might interest others. He can be contacted at email@example.com.