Everything you never wanted to know about circular analysis, but were afraid to ask

Abstract

Over the past year, a heated discussion about ‘circular' or ‘nonindependent' analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in ≤100 words per question. Although divergent views remain on some of the questions, there is also a substantial convergence of opinion, which we have summarized in a consensus box. The box provides the best current answers that the five authors could agree upon.

Introduction

Brain imaging produces very large data sets of brain activity measurements. However, the neuroscientific conclusions in papers are typically based on a small subset of data. The necessary selection—unless carefully accounted for in the analysis—can bias and invalidate statistical results (Vul et al, 2009a; Kriegeskorte et al, 2009).

The large number of brain locations measured in parallel allows us to discover brain regions with particular functional properties. However, the more we search a noisy data set for active locations, the more likely we are to find spurious effects by chance. This complicates statistical inference and decreases our sensitivity to true brain activation. In functional magnetic resonance imaging, the goal is typically twofold: (1) to identify voxels that contain a particular effect and (2) to estimate the size of the effect, typically within a region of interest. Whether widely used analyses meet the resulting statistical challenges has been hotly debated in the past year.

Let us consider the first goal: finding brain regions that contain a particular effect. For example, we may wish to answer questions such as: Which voxels respond more to faces than houses? Or, in which voxels does the face-house contrast correlate with IQ across subjects? The use of many null-hypothesis tests across brain locations presents a multiple testing problem: the more voxels that are tested, the greater the family-wise error rate (FWE), i.e., the probability that one or more voxels will pass the significance threshold by chance even when there are no true effects (false-alarm voxels). A number of statistical methods have been developed to control the FWE (for a review, see Nichols and Hayasaka, 2003).

The Bonferroni method increases the significance threshold for each voxel to ensure that the FWE does not exceed, say 0.05. However, as Bonferroni's method does not account for image smoothness, it is overly conservative and not optimally sensitive. Random field theory methods (Worsley et al, 1992; Friston et al, 1994) adjust for spatial correlation between voxels to achieve greater sensitivity (i.e., power—the probability that a truly active voxel will be identified as such). Although voxel-wise methods detect individual voxels, cluster-wise methods (Poline and Mazoyer, 1993) report as significant clusters (contiguous sets of voxels that all exceed a primary threshold) that are larger than a predetermined cluster-size threshold (chosen to ensure a 5% FWE for clusters).

Instead of limiting the probability of any false alarms (i.e., the FWE), false-discovery rate methods (Genovese et al, 2002) limit the average proportion of false alarms among the voxels identified as significant. This approach promises greater sensitivity when there are effects in many voxels. When used appropriately, these methods solve the multiple testing problem and ensure that we are unlikely to mistake an inactive region for an active region.

The second goal is estimating the size of the effect. For example, we may wish to answer questions such as: How strongly do these voxels respond to faces? Or, how highly does the activation contrast in this region correlate with IQ across subjects? Unfortunately, we cannot accurately address such questions by simply analyzing the selected voxels without worrying about the selection process. The effect-size statistics need to be independent of the selection criterion; otherwise the results will be affected by ‘selection bias.' For intuition, imagine the data were pure noise. If we select voxels by some criterion, those voxels are going to better conform to that criterion than expected by chance (for randomly selected voxels). Even if the selected voxels truly contain the effect of interest, the noise in the data will typically have pushed some voxels into the selected set and some others out of it, thus inflating the apparent effect in the selected set.

This problem has long been well-understood in theory, but is not always handled correctly in practice. Variants of bias due to selection among noisy effect estimates affect many parts of science. Just like voxels are selected for inclusion in an ROI, so studies are selected for publication in scientific journals (Ioannidis, 2005; 2008). In either case, the selection criterion is effect strength, and effect estimates are inflated as a result.

Vul et al (2009a) suggested that cross-subject correlation neuroimaging studies in social neuroscience are affected by ‘nonindependence' (also see Vul and Kanwisher, 2010). Kriegeskorte et al (2009) discuss the problem of ‘circularity' more generally as a challenge to systems neuroscience.

These authors argued that effect estimates and tests based on selected data need to be independent of the selection process, and that this can be ensured by using independent data for selection (e.g., using half of the data to select signal-carrying voxels, and the other half to estimate the signal) or by using inherently independent functional or anatomic selection criteria.

Although there is little controversy about the basic mechanism of selection bias, the 2009 papers have sparked a debate about exactly which analysis practices are affected and to what degree (Diener, 2009; Nichols and Poline, 2009; Yarkoni, 2009; Lieberman et al, 2009; Lazar, 2009; Lindquist and Gelman, 2009; Barrett, 2009; Vul et al, 2009b; Poldrack and Mumford, 2009). Herein, we collaboratively assembled and then individually answered a series of questions on circular analysis to clarify points of agreement and disagreement. Each answer is ≤100 words. We hope to contribute to a convergence within the community toward statistical practices that ensure that systems and cognitive neuroscience remain solidly grounded in empirical truth.

Scope of the problem
(1) Is circular analysis a problem in systems and cognitive neuroscience?
NK	Yes. A significant minority of papers is affected by distortions, which range from slight to severe.
ML	The term ‘circular analysis' covers a wide variety of situations, the severity of which range from fatal to relatively benign depending on the situation and the type of information one is seeking. For example, using the same data to both train and test a classifier would be a huge problem, disqualifying any subsequent results. However, reporting the effect size over regions that survive a multiple comparisons analysis is less serious as long as the focus of the analysis is to detect regions with nonzero effect. However, even these estimates can be problematic if overinterpreted by readers/reviewers.
TN	Yes.
RP	Yes.
EV	Yes.
(2) How widespread are slight distortions and serious errors caused by circularity in the neuroscience literature?
NK	Without reanalyses, the answer is elusive. My guess is that slight distortions are more widespread than severe distortions—but erroneous conclusions can result in either case.
ML	Most cases of circular analysis that I have seen in the literature relate to the practice of reporting effect size estimates over regions that survive a multiple comparisons analysis. If the focus of the analysis is null-hypothesis testing, then providing these values is not necessarily problematic. However, they must always be reported in their appropriate context, i.e., as post hoc estimates of effect size. The most widespread distortion is probably an improper description of these estimates. This distortion has the potential of becoming serious if readers/reviewers read too much into these estimates and overinterpret their value.
TN	False positives due to circularity are minimal; biased estimates of effect size are common. False positives due to brushing off the multiple testing problem (e.g., ‘P<0.001 uncorrected' and crossing your fingers) remain pervasive.
RP	I think that slight distortions due to circularity are fairly common. I have no doubt that there have been serious errors due to circularity, including publication of empirical findings that are completely due to noise, but I think that this is relatively uncommon. I think that such serious problems are most likely to arise when circular analyses are combined with the use of uncorrected (or incorrectly corrected) whole-brain analyses.
EV	Slight distortions seem very common (maybe one-third to one-half of published functional magnetic resonance imaging (fMRI) papers contains some such distortion). I think that frequency falls off with the gravity of error: Fortunately, extreme cases (circular analysis on data selected using an inappropriately corrected selection procedure—potentially producing results from pure noise) are uncommon. Nevertheless, for the bulk of results obtained using circular methods, it is impossible to assess how large the distortions are until those results are reanalyzed using unbiased procedures.
Estimating effect sizes
(3) Are circular estimates useful measures of effect size?
NK	No.
ML	The standard approach of estimating effect sizes for voxels that survive an appropriate multiple comparisons threshold is biased and tends to overestimate the true effect size. In certain situations (e.g., when the effect size is large and/or the variance is small), this bias may be small and the estimate can still provide useful information. As a general principle, using an estimate with some bias is usually acceptable if it decreases the variance compared with other possible estimates. However, in this case, I believe better unbiased estimates are available that are preferable (see Question 6).
TN	No. However, the bias in estimated effect size is a variable and diminishes to zero with increasing true effect size (i.e., increasing ratio of effect magnitude to s.d.).
RP	Circular estimates will generally inflate the estimated effect size in comparison with noncircular analysis. Thus, I do not believe that circular estimates are valid measures of effect size.
EV	No—they are inflated to an unknown degree and are thus meaningless for inference.
(4) Should circular estimates of effect size be presented in papers and, if so, how?
NK	No.
ML	If the goal of the study is to estimate effect sizes, then using circular estimates is not appropriate, and noncircular analyses are preferable. If the goal is hypothesis testing, I do not see an inherent problem in presenting them as long as they are placed in their appropriate context. That said, I am not sure how meaningful they are by themselves and I would urge readers not to overinterpret their value. In general, it may be useful to present any effect size estimate as confidence intervals, so that readers can see for themselves how much uncertainty is related to the point estimate.
TN	Yes, if well described as circular and the detection of the effect itself was based on a valid inferential procedure.
RP	In general, they should not be presented unless accompanied by parallel noncircular effect size estimates (Poldrack and Mumford, 2009). However, if a researcher insists on presenting circular effect size estimates, then they should be presented with the clear caveat that they were estimated in a circular manner.
EV	No. As circular estimates can only be misleading, there is no good reason to present them. Nevertheless, if for some reason circular estimates are presented, they should come with a disclaimer to warn readers that they are looking at an uninterpretable number.
(5) Are effect size estimates important/useful for neuroscience research, and why?
NK	Yes, estimation of quantities is a key element of all sciences. Hypothesis testing by itself (A>0? A>B?) yields an impoverished picture of the data and a loss of scientific insight. It also makes it more difficult to relate results across multiple studies and to assess the relevance of demonstrated relationships for practical applications (e.g., the diagnostic power of a given fMRI paradigm).
ML	Most neuroimaging studies to date have been focused on null-hypothesis testing and relatively little interest has been placed on estimating effect sizes. Whether that will change in the future I leave for the others to discuss. However, even in the null-hypothesis framework, these estimates are useful. For example, accurate effect size estimates are required for performing power analysis, which in turn are used to determine appropriate sample sizes for future experiments. Using biased estimates of effect size will ultimately lead to underpowered studies which can have serious ramifications.
TN	Depends on the question. Life is too short and research budgets too limited to require double experiments, n subjects to make inference on the location of an effect, another n subjects to get unbiased effect size estimates (it must be noted that splitting the data from n subjects does not yield two independent data sets). Hence, the researcher has to choose whether they are after inference on location of an effect, or estimation of effect size assuming a known location.
RP	I think that effect size estimates may be useful in some circumstances. When we report our data, we generally wish to convey information regarding the strength of the effect, to provide a guide toward its importance. For example, we will be more impressed with a finding that activity in a particular region predicts 20% of the variance in a psychological trait than if it predicts 1%. That said, I do not think we have a really good notion of how big an effect needs to be to be considered ‘important' in neuroimaging.
EV	Yes, very much so. Null-hypothesis testing is insufficient for most goals of neuroscience because it can only indicate that a brain region is involved to some nonzero degree in some task contrast. This is likely to be true of most combinations of task contrasts and brain regions when measured with sufficient power. To determine how the brain produces cognition and behavior, cognitive neuroscience must answer questions such as: ‘which area is most responsible for this cognitive function,' or ‘what cognitive function is this area most involved in,' etc. These questions require evaluating effect sizes and comparing them across regions and tasks.
(6) What is the best way to accurately estimate effect sizes from imaging data?
NK	If the neuroscientific question requires selection of a subset of the data (e.g., a region of interest (ROI) in the brain), then the selection process must not bias the effect estimate. We can either show that the effect statistic is inherently independent of the selection process and thus unaffected by selection (e.g., ROI definition by an anatomic or other statistically independent criterion), or we can use independent data (i.e., replications of the same experimental conditions) to estimate the effect for the selected subset of the data.
ML	There are a number of ways to accurately estimate effect sizes. For example, this can be carried out using anatomically defined ROIs or using various forms of cross-validation. We have in previous work suggested a method that selects voxels based on whether they show significant individual differences in the population (Lindquist et al, 2009) and a method based on using a multilevel Bayesian mixture model (Lindquist and Gelman, 2009). Developing methodology for accurately estimating effect sizes promises to be an area of active research in the future.
TN	Assume a known location and ROI for the effect, average data within that ROI, and make univariate inference on that data.
RP	Using independent/noncircular approaches. Reasonable approaches include the use of prespecified anatomic ROIs, independent localizer scans, split-half, or cross-validation methods. It may also be reasonable to use regions identified from one contrast in a factorial design to estimate the effect of an orthogonal contrast, so long as it is confirmed that the effective regressors defined by the contrasts are truly orthogonal (which may fail for some rapid event-related designs; Kriegeskorte et al, 2009).
EV	Effect sizes should be estimated using unbiased, independent data. When considering a priori defined regions, this can be carried out with independent functional or anatomic localizers; if no ROIs are known a priori, independence can be achieved by splitting a data set and using various cross-validation methods.
(7) What makes data sets independent? Are different sets of subjects required?
NK	The statement ‘data sets A and B are independent' requires qualification: What is independent between the data sets?
	(1) …the within-subject measurement noise (requiring different sets of runs)?
	(2) …the across-subject variation (requiring different subjects)?
	(3) …everything?
	We never want kind (3): total independence would mean there are no replicating true effects between data sets. We sometimes want independence of kind (2): for random-effects inference generalizing to the population. We always want independence of kind (1): for independent fitting and testing of single-subject models (e.g., defining a subject-specific ROI, i.e., binary weighting, and/or training a pattern classifier, i.e., continuous weighting).
ML	It depends on what type of question you want to answer. If you are interested in population inference (or prediction on future subjects) then splitting the data across subjects is required to ensure independent data sets. If, however, you are concerned with single subject inference (or prediction) then splitting within that subject will suffice. The problem of determining independence in the group setting is interesting, in part because statisticians rarely do test-training type splits and nonstatisticians rarely worry about random effects. Here we need to think about both.
TN	Only data sets collected on distinct individuals can be assured to be independent. Splitting an individual's data (e.g., using run 1 and run 2 to create two data sets) does not yield independence at the group level, as each subject's true random effect will correlate the data sets. One exception is when the null hypothesis is true, as there are no random effects to correlate the pairs. Another exception is a fixed effects analysis, as there are no random effects to cause problems.
RP	For group analysis, I think that independence will generally require different subjects, although cross-validation approaches such as ‘leave one subject out' mean that one does not have to run an entirely different group. I think that simulated analyses in which the null hypothesis is made true (e.g., by randomly permuting the outcome variable across subjects in a correlation analysis) are a good way to assess the validity of any proposed analysis.
EV	This depends on the inferences one wants to make at the group level. To claim that an effect exists in a specific anatomic region across the population (‘Talairach coordinates (x, y, z) activate in response to reward prediction error'), one must validate with independent subjects. In contrast, to claim that a subject-specific ROI contains a certain effect across the population (‘the subject-specific FFA respond more to faces than bodies'), one need only validate with independent data from the same subjects: The signal from an independently defined region can be compared at the group level using standard univariate statistics.
Beyond effect sizes: other uses of nonindependent selective analyses
(8) What information can one glean from data selected for a certain effect?
NK	Information independent of the selection criterion. However, it is not trivial to assess what aspects of the data are subtly related to the selection criterion and what aspects are strictly independent of it.
ML	It depends on how the data were selected. In the hypothesis-testing framework, one can state that given the assumptions, these brain areas show a nonzero effect. One should resist the temptation of trying to extract much more information than that.
TN	If the effect used for selection is nearly statistically independent from the tested effect (e.g., a main effect and an interaction), there are no limitations, as the circularity problem will be minimized. Totally dependent selection (e.g., pick the voxel with peak t-value) is essential for data visualization to ensure result is not due to outliers.
RP	These data cannot inform us regarding the strength of the effect, but we can gain general information about the nature of the effect and the degree to which assumptions of the statistical analyses may be violated.
EV	Only information independent of that selected effect. Consider data selected for (A+B)>0. Those data cannot be used to evaluate the magnitude of the A+B effect because the resulting measure will be inflated and biased. Those data also cannot be used to evaluate the dispersion or residuals (or model error) around the A+B effect because the measured dispersion will be biased to be lower. However, those data can be used to evaluate independent effects; for instance, the contrast A−B is often largely independent (given a balanced design matrix, but care must be paid to ensure that superficially orthogonal contrast vectors are indeed independent).
(9) Are visualizations of nonindependent data helpful to illustrate the claims of a paper?
NK	They certainly help ‘tell the story'—an important part of scientific communication. To this end, however, it is entirely legitimate to include plots designed by hand. If a plot claims to present empirical evidence, the evidence should not be distorted. Nonindependent selection will tend to ‘clean up,' appearing to give us both a view of the data and a clear illustration of our hypothesis. However, selection is akin to morphing between a data-based plot showing actual results and a hand-drawn plot illustrating the hypothesis. Although each of these two is useful in its pure form, their amalgamation is misleading.
ML	I am hesitant to recommend less visualization, as they are critical for both model diagnosis and presentation of results. A visualization of nonindependent data can help determine whether outliers are affecting the estimate. However, they can also lull readers' into believing the effect is stronger than in reality. Therefore, it is useful to explicitly state that the plot is intended for diagnostic purposes and caution that the strength of the relationship not be overinterpreted. In general, always make visualizations as part of your analysis, but think carefully about which to include in your paper and how to present them.
TN	For experts in the field of neuroimaging, yes, such data visualization is crucial. For nonspecialist (i.e., nonimaging-specific) journals, they should perhaps be relegated to supplementary material.
RP	In some cases, yes. For example, it can be difficult to interpret a significant interaction in a 2 × 3 ANOVA (analysis of variance) without visualizing the specific pattern that drives the result. In addition, I think that nonindependent scatterplots should always be visualized (although not presented in a paper) for correlation analyses, to assess the presence of outliers. The failure to see any obvious outliers does not guarantee that the result is correct, but the presence of outliers should result in additional analyses to ensure that the result is not reliant upon a single observation (which occurs all too often with fMRI correlations).
EV	Sometimes. However, visualizations of nonindependent data are not more helpful than hand-drawn illustrations of the effects of interest. Moreover, visualizations of nonindependent data are more misleading, as they appear to have inferential weight, while they have none, this is exacerbated when nonindependent data are plotted with measures of dispersion or error.
(10) Should data exploration be discouraged in favor of valid confirmatory analyses?
NK	No, data exploration is essential to scientific discovery. The cycle of exploration and confirmation can be closed within a single study, by using independent replications of the experiment. Central claims of a paper should be supported by valid confirmatory analyses. Additional unconfirmed exploratory results can be described as well, but it should be made clear that they are in need of future confirmation. Papers presenting only unconfirmed exploratory results should be discouraged. Unconfirmed results should never be cited without the caveat that they are yet to be confirmed.
ML	No. I do not view these types of analysis to be in direct competition with one another. There is a place for both, especially in an emerging field, such as neuroimaging. The important thing is that it is clearly stated whether a result is obtained through data exploration or confirmatory analysis.
TN	No, they are both required. Exploratory data analysis (EDA) is required to ensure the data conform to the distribution assumptions of the model (specifically, is not outlier ridden), and discouraging EDA leads to black-box usage of software and analysis tools. If you cannot trust a researcher to stick to their a priori hypotheses, can you even trust that their results are not all Photoshop?
RP	Our understanding of brain function remains incredibly crude, and limiting research to the current set of models and methods would virtually guarantee scientific failure. Exploration of new approaches is thus critical, but the findings must be confirmed using new samples and convergent methods. For example, in the literature on resting-state fMRI, early findings using exploratory methods have been confirmed in subsequent studies using various analysis techniques. Had the field insisted on a single approach to fMRI analysis (e.g., using task-based fMRI with the general linear model), the valuable insights from this body of work would have been missed.
EV	Absolutely not—data exploration is vital for science. However, it should be treated and presented as exploration: if a novel effect or phenomenon is suggested by an exploratory analysis, it should be validated by an independent confirmatory analysis before being treated as an established finding (i.e., being cited, incorporated into meta-analyses, etc.) Without independent validation, the outcomes of an exploratory analysis should be treated as a well-reasoned speculation.
(11) Is a confirmatory analysis safer than an exploratory analysis in terms of drawing neuroscientific conclusions?
NK	An exploratory approach is more prone to selection bias: The more we explore, the greater the chance to find something in the noise. However, a confirmatory approach is more prone to model-misspecification bias: The more we assume, the greater the chance that our conclusions are built on sand.
ML	It depends on what type of conclusion you want to make. If the goal of the study is to test a certain null hypothesis with a certain false-positive rate than confirmatory analysis is safer. At the end of the day, confirmatory and exploratory analyses provide different ways of looking at the data. It is important to realize that the conclusions one can make are different and the results must be interpreted in their own contexts.
TN	Of course; EDA offers no inference.
RP	It depends on what risk one is protecting against. With regard to the risk of false positives, I think confirmatory analyses are probably safer. However, with regard to the more general risk of misunderstanding how the brain works, confirmatory analyses may be riskier as they bias us toward a very small portion of the hypothesis space.
EV	They each have their strengths and weaknesses. A confirmatory analysis is blind to new effects and hypotheses, whereas an exploratory analysis is susceptible to spurious fluctuations in the data. Both are necessary for scientific progress. However, although working within the null-hypothesis testing framework, confirmatory analyses provide the only legitimate basis for inference.
(12) What makes a whole-brain mapping analysis valid? What constitutes sufficient adjustment for multiple testing?
NK	Whole-brain analysis is exploratory with respect to brain space: we can discover new functional regions. The field has developed powerful methods that both explore the whole brain and confirm the result—with a single data set. These methods account for the multiple tests performed across locations. They are valid when the family-wise error rate (FWE) or false-discovery rate (FDR) is ≤5%. An uncorrected threshold of P<0.001 does not usually ensure this. Permutation methods can help reduce assumptions or estimate a given method's actual error rate (thus checking its assumptions).
ML	When performing a standard whole-brain analysis, with separate hypothesis tests at each voxel, researchers should always use appropriate corrections for multiple comparisons. These include techniques that either control for the FWE or the false detection rate. In, addition it is important that the manner in which the correction has been performed be made explicit in the article, as this will guide the reader in interpreting the results.
TN	Control of FWE or FDR false-positive risk over an a priori defined analysis mask, using an a priori specified statistic (e.g., voxel-wise or cluster-wise inference).
RP	I am satisfied with the use of any method that has been established to control FWE or FDR. I find nonparametric approaches (Nichols and Holmes, 2002) most appealing as they rely upon the fewest assumptions; they are computationally intensive, but increases in computing power have made them feasible for nearly all researchers.
EV	Although P<0.05 is just convention, it is a useful convention that has helped science proceed for the past 80 years, as such, I think it should be maintained until we have a justified alternative. In the meantime, I consider whole-brain analyses valid if they are corrected to keep the FWE below 5%. Unfortunately, such corrections are sometimes described cryptically, used incorrectly, and greatly reduce the power of a neuroimaging analyses. To avoid these problems, I think testing specific neuroanatomical hypotheses (using functionally or anatomically defined regions of interest) is generally preferable to exploratory whole-brain analyses.
(13) How much power should a brain-mapping analysis have to be useful?
NK	A brain-mapping analysis (or any other test) is only as useful as it is powerful (assuming that its specificity is controlled at 5% false positives). Lower power has three negative effects: (1) by definition: lower chance of finding something given that its there, (2) lower information gained about the presence or absence of an effect (at 5% power, no information gained at all), and (3) lower probability of a true effect given a positive finding (for any given prior probability of the effect), and thus a greater proportion of false positives in the literature due to publication bias for positive results.
ML	It is difficult to give an exact lower bound on the amount of power in 100 words, as it ultimately depends on the goal of the study. Perhaps the best answer is ‘as much as possible'… Neuroimaging studies tend to be underpowered, as scanning subjects is expensive and time consuming. Underpowered studies can lead to an increased number of false negatives, as well as a greater variability in effect size estimation. Hence, in studies with small sample size, large effect sizes may simply reflect the influence of random variation (Lindquist and Gelman, 2009).
TN	The chance of detecting one or more true-positive voxels (or clusters) should be 80% or better. Easy to say, but such power is nearly impossible to calculate because of the myriad possible configurations of alternative hypotheses.
RP	Enough power to find an interesting effect, which begs the question of how big an effect has to be to be ‘interesting' (to which I do not think we have a good answer).
EV	Low-powered brain-mapping paints a misleading picture of neural function. Low-powered studies tend to find few punctate regions even if the underlying effect is diffuse over many, large areas; they amplify the adverse effects of publication bias; and they yield large distortions when combined with circular effect-size estimates. It is worth striving for ∼80% statistical power. Such statistical power can be more easily achieved when testing specific neural hypotheses rather than conducting exploratory analyses over the whole brain.
Guidelines for publication
(14) In which circumstances are nonindependent selective analyses acceptable for scientific publication?
NK	Let us say ‘never,' and take an important step toward ensuring that our field remains solidly grounded in empirical reality. If there are aspects of the results that are demonstrably independent of the selection process, those aspects can be presented in isolation (making the selective analysis independent). If independent aspects do not exist, then the results are not useful. If isolating the independent aspects is difficult, data can almost always be either divided or replicated. Exceptions could be made if the cost outweighs the benefit of taking these steps, central claims are not concerned, and the analysis is clearly marked as circular.
ML	It ultimately depends on the purpose and goals of your study. When training a classifier, I would be hard pressed to find a situation in which it is acceptable. The same is true when the goal is to obtain an accurate effect size estimate. When performing null-hypothesis testing, I think it is acceptable to report an effect size as long as appropriate guidelines for interpreting the value are provided. It is, however, important that readers not be encouraged to inflate the importance or meaning of the reported estimates.
TN	Circular inference is never acceptable, but reporting circular effect sizes and plotting nonindependent data can serve useful purposes when clearly marked as such.
RP	As the primary finding of a paper, I find nonindependent analyses unacceptable. If presented in support of particular interpretations of the results, I think that they are potentially acceptable so long as they are clearly labeled as nonindependent.
EV	I think nonindependent selective analyses are never informative, because they will be biased to an unknown degree. However, if there is an exceptional reason for including such a biased analysis in a paper, it must be clearly demarcated as nonindependent and presented with the caveat that the biased result is inferentially meaningless.

The authors declare no conflict of interest.