We argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton’s ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations—as well as their statistical consequences—establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.
A superficial assessment of the published literature suggests that statisticians rarely agree on anything. Different schools—mostly frequentists, likelihoodists and Bayesians—have fought one another tooth and nail for decades, debating the meaning of ‘probability’, arguing about the role of prior knowledge, disputing the value of objective versus subjective analyses and disagreeing about the primary goal of inference itself: whether researchers should control error rates, update beliefs, or make coherent decisions. Fundamental disagreement exists not only between the different statistical schools, but also within the same school. For example, within the frequentist school, there is the perennial debate between those who seek to test hypotheses through P values and those who emphasize estimation through confidence intervals; and within the Bayesian school, Jack Good’s claim that there are 46,656 varieties of Bayesians may prove to be an underestimate1 (but see also ref. 2).
The disagreement also manifests itself in practical application whenever multiple statisticians and practitioners of statistics find themselves independently analysing the same dataset. Specifically, recent ‘multiple-analyst’ articles show that statisticians rarely use the same analysis, and they often draw different conclusions, even for the exact same dataset and research question3,4,5,6,7. Deep disagreement is also exhibited by contradictory guidelines on P values8,9,10,11,12,13. Should practitioners avoid the phrase ‘statistically significant’? Should they lower the P value thresholds, or justify them, or abandon P values altogether? And, if P values are abandoned, what should replace them? With statisticians fighting over these fundamental issues, users of applied statistics may be forgiven for adopting a wait-and-see attitude and carrying on as usual.
In this Perspective, we claim that, besides the numerous disputes and outstanding arguments, statisticians might agree on a set of scientific norms. We bring these norms to the fore, as we believe that they have considerable relevance for the practice of statistics in the social and behavioural sciences. The norms that we believe should guide statistical practice are communalism, universalism, disinterestedness and organized scepticism, which are the four scientific norms proposed by Merton14 (originally published in 1942; see Box 1 for a detailed overview of the Mertonian norms and Box 2 for an overview of how each statistical procedure discussed here fulfills these norms).
By visualizing data, researchers can graphically represent key aspects of the observed data as well as important properties of the statistical model applied.
Benefits and examples
Data visualization is important in all phases of the statistical workflow. In exploratory data analysis, data visualization helps researchers to formulate new theories and hypotheses15. In model assessment, data visualization supports the detection of model misfit and guides the development of appropriate statistical models16,17,18,19,20. Finally, once the analysis is complete, visualization of data and model fit is arguably the most effective way to communicate the main findings to a scientific audience21.
For an example of how data visualization facilitated the development of a new hypothesis, consider the famous map of the distribution of deaths from cholera created by London anaesthetist John Snow during the cholera outbreak in Soho, London, in September 1854. To trace the source of the outbreak, John Snow created a dot map that displayed the homes of the deceased as well as the water pumps in the neighbourhood (Fig. 1). The scatter of the data showed that the deaths clustered around a particular water pump in Broad Street, suggesting that the disease was waterborne instead of airborne22. In response to John Snow’s request, the pump was disabled by removing its handle, which immediately ended the neighbourhood epidemic. It was later discovered that the well belonging to the pump was contaminated with sewage, which caused the outbreak in the neighbourhood.
For an example of how data visualization can reveal model misspecification, consider Anscombe’s quartet23 shown in Fig. 2. The four scatter plots all have identical summary statistics (that is, mean, standard deviation and Pearson correlation coefficient values). By visually inspecting the panels, it becomes obvious that the bivariate relation is fundamentally different for each panel24.
Since William Playfair (1759–1823) invented the first statistical graphs—such as line graphs and bar charts25—data visualization has become an essential part of science. Today, graphs are part of most statistical software packages and have become an indispensable tool to perform certain analyses (that is, principal component analysis or prior and posterior predictive checks) or for handling big datasets (that is, through cluster analysis26). Technology now enables us to go beyond static visualizations and display the dynamic aspects of the data, for example, using the software packages R Shiny27 or iNZight28.
Despite the obvious benefits, data visualization also offers the opportunity to mislead, for example, when displaying spurious patterns by either expanding the scale to minimize variation, or by minimizing the scale to accentuate differences29,30,31.
Furthermore, the informativeness of a graph often depends on the design capabilities of the researcher and how much thought they put into what information should be communicated. Scientists without programming experience often find themselves constrained by the options offered in standard graphics software. However, the example of Anscombe’s quartet shows that even the simplest plots can be highly informative.
There are no uniform guidelines as to when and which graphical representations should be used. However, there is a fundamental principle of good statistical graphics according to Tufte (page 92 of ref. 32): “Above all else show the data” (that is, minimize non-data elements). In general, scientists should aim to create a graph that is as clean, informative and as complete as possible. These characteristics are also emphasized in the ASA Ethical Guidelines33. The guidelines mention that, to ensure the integrity of data and methods, the ethical statistician “[i]n publications and reports, conveys the findings in ways that are both honest and meaningful to the user/reader. This includes tables, models, and graphics” (page 3 of ref. 33).
Beyond that, guidelines depend on the individual aspects of the data (that is, the complexity of the data and experimental design) and context (compare with ref. 34); here we refer the interested reader to the numerous manuals describing good practices in graphical representation of statistical information32,35,36,37,38,39,40.
Quantifying inferential uncertainty
By reporting the precision with which model parameters are estimated, the analyst communicates the inevitable uncertainty that accompanies any inference from a finite sample.
Benefits and example
Only by assessing and reporting inferential uncertainty is it possible to make any claim about the degree to which results from the sample generalize to the population. For example, Strack et al.41 studied whether participants rate cartoons to be funnier when they hold a pen with their teeth (which induces a smile) instead of holding it with their lips (which induces a pout). On a ten-point Likert scale, the authors observed a raw effect size of 0.82 units. For the interpretation of this result, it is essential to know the associated inferential uncertainty. In this case, the 95% confidence interval ranges from −0.05 to 1.69, indicating that the data are not inconsistent with a large range of effect-size estimates (including effect sizes that are negligible or negative).
In almost all statistics courses, students are taught to provide not only the summary of statistical tests (such as F, t and P values and the associated degrees of freedom), but also parameter point-estimates (that is, regression weights, effect sizes) and their associated uncertainty (that is, standard error, posterior distribution, confidence intervals, credible intervals). Nevertheless, there exists a gap between what is taught and what is practiced. Studies of published articles in physiology20, the social sciences42 and medicine43,44 revealed that error bars, standard errors or confidence intervals were not always presented. Popular metrics such as Cronbach’s alpha (a measure of test score reliability) are also rarely presented with a measure of inferential uncertainty.
We believe that there are no acceptable excuses for omitting a measure of inferential uncertainty in any report.
Although not a limitation as such, it should be noted that inferential uncertainty always needs to be quantified relative to the inferential goal—does a researcher want to generalize across people, stimuli, time points or another dimension? The proper way of computing standard errors depends on the researcher’s purpose.
Various guidelines strongly recommend that effect-size estimates are accompanied by measures of uncertainty in the form of standard errors or confidence intervals. For example, the publication manual of the American Psychological Association (6th edn, page 34) states: “When point estimates (that is, sample means or regression coefficients) are provided, always include an associated measure of variability (precision), with an indication of the specific measure used (that is, the standard error)”. Furthermore, the International Committee of Medical Journal Editors45 explicitly recommend to “[w]hen possible, quantify findings and present them with appropriate indicators of measurement error or uncertainty (such as confidence intervals)” (page 17 of ref. 45).
Assessing data preprocessing choices
By assessing the impact of plausible alternative data preprocessing choices (that is, examining the ‘data multiverse’46), the analyst determines the extent to which the finding under scrutiny is either fragile or sturdy.
Benefits and example
A data multiverse analysis reveals the fragility or sturdiness of the finding under plausible alternative data preprocessing choices. This prevents researchers from falling prey to hindsight bias and motivated reasoning, which may lead them to unwittingly report only the preprocessing pipeline that yields the most compelling result47,48. But even a completely unbiased analysis will benefit from a data multiverse analysis, as it reveals uncertainty that would otherwise remain hidden.
For example, Steegen et al.46 re-examined the results of Durante et al.49, who reported an interaction between relationship status (that is, single or not) and menstrual cycle (that is, fertile or not) on reported religiosity. After applying a series of 180 different data preprocessing procedures (that is, five different ways to split women into high versus low fertility), the multiverse reanalysis showed that the resulting 180 P values were distributed uniformly between 0 and 1, indicating that the reported interaction is highly fragile.
The idea of assessing sensitivity to data-preprocessing choices dates back at least to De Groot (page 190 of ref. 47) and Leamer (page 308 of ref. 50) and was revived by Simmons et al.48 and by Steegen et al.46. In the field of functional magnetic resonance imaging, both Carp51 and Poldrack et al.52 emphasized the hidden influence of different plausible preprocessing pipelines. In psychology, recent applications are Bastiaansen et al.3 and Wessel et al.53. Nevertheless, the overwhelming majority of empirical articles does not report the results of a data multiverse analysis.
A pragmatic limitation of the data multiverse lies in the extra work that it entails. Another limitation can be found in ambiguities surrounding the definition of the data multiverse. The analyst has to determine what constitutes a sufficiently representative set of preprocessing choices and whether all preprocessing choices are equally plausible, such that they should be given equal weight in the multiverse analysis. A final limitation is that it is not always clear how to interpret the results of a data multiverse analysis. Interpretation can be facilitated with certain graphical formats that cluster related pipelines (that is, specification curves)54.
Some specific guidelines on assessing data preprocessing choices are offered by Simmons et al.48 (see the requirements for authors, numbers 5 and 6), but it is difficult to provide general guidelines as “[…] a multiverse analysis is highly context-specific and inherently subjective. Listing the alternative options for data construction requires judgement about which options can be considered reasonable and will typically depend on the experimental design, the research question, and the researchers performing the research” (page 709 of ref. 46). More general guidelines that relate exclusively to the reporting of preprocessing choices are given in the ASA Ethical Guidelines33. These mention that, to ensure the integrity of data and methods, the ethical statistician “[w]hen reporting on the validity of data used, acknowledges data editing procedures, including any imputation and missing data mechanisms” (page 2 of ref. 33).
Reporting multiple models
By assessing the impact of plausible alternative statistical models (that is, examining the model multiverse), the analyst gauges the extent to which a statistical conclusion is either fragile or sturdy.
Benefits and example
Similar to the data multiverse analysis discussion in the previous section, a model multiverse analysis examines the fragility or sturdiness of the finding under plausible alternative statistical modelling choices. Modelling choices comprise differences in estimators and fitting regimes, but also in model specification and variable selection. Reporting the outcomes of multiple plausible models reveals uncertainty that would remain hidden if only a single model were entertained. Moreover, this practice protects analysts against hindsight bias and motivated reasoning, which may unwittingly lead them to select the single model that produces the most flattering conclusion. For example, Patel et al.55 quantified the variability of results under different model specifications. They considered 13 clinical, environmental and physiological variables as potential covariates for the association of 417 self-reported, clinical and molecular phenotypes with all-cause mortality. As a consequence, they computed P values for 213 = 8,192 models and examined the instability of the inference, which they call the ‘vibration of effects’.
As was the case for the construction of the data multiverse, a pragmatic limitation of the model multiverse lies in the extra work that it entails for the analyst as well as the reader. Recent work suggests that the number of plausible models can be very large4,7. Multiverses also vary in their informativeness, and readers need to assess themselves whether a multiverse features notably distinct models or just runs the essentially same model multiple times. Model spaces can be overwhelming; any single analyst will naturally be drawn towards the subset of models that they are familiar with (or, unwittingly, the subset of models that yields the result that is most flattering or most in line with prior expectations). Moreover, Del Giudice et al. (page 5 of ref. 58) argue that “By inflating the size of the analysis space, the combinatorial explosion of unjustified specifications may, ironically, exaggerate the perceived exhaustiveness and authoritativeness of the multiverse while greatly reducing the informative fraction of the multiverse. At the same time, the size of the specification space can make it harder to inspect the results for potentially relevant findings. If unchecked, multiverse-style analyses can generate analytic ‘black holes’: Massive analyses that swallow true effects of interest but, due to their perceived exhaustiveness and sheer size, trap whatever information is present in impenetrable displays and summaries.”
Because the construction of the model multiverse depends on the knowledge and expertise of the analyst, it is challenging to provide general guidelines. However, for relatively simple regression models, clear guidelines do exist55,59. Furthermore, Simonsohn et al.54 suggested a specification curve analysis, and Dragicevic et al.60 suggest interactive ways of presenting the results. The ASA Ethical Guidelines33 mention that, to meet the responsibilities towards funders and clients, the ethical statistician “[t]o the extent possible, presents a client or employer with choices among valid alternative statistical approaches that may vary in scope, cost, or precision” (page 3 of ref. 33). However, the ASA does not mention that researchers share the same responsibility towards their scientific colleagues, although this may be implicit.
One general recommendation for constructing a comprehensive model multiverse is to collaborate with statisticians who have complementary expertise, bringing us to the next section.
Involving multiple analysts
By having multiple analysts independently analyse the same dataset, the researcher can decrease the impact of analyst-specific choices regarding data preprocessing and statistical modelling.
Benefits and example
The multiple-analysts approach reveals the uncertainty that is due to the subjective choices of a single analyst and promotes the application of a wider range of statistical techniques. When the conclusions of the analysts converge, this bolsters one’s confidence that the finding is robust; when the conclusions diverge, this undercuts that confidence and stimulates a closer look at the statistical reasons for the lack of consensus.
The multiple-analysts approach was used, for example, in a study by Silberzahn et al.7, in which 29 teams of analysts examined, using the same dataset, whether the skin tone of soccer players influences their probability of getting a red card. Although most of the analysis teams reported that players with a darker skin tone have a higher probability of getting a red card, some of the teams reported null results. The analysis approach used by the teams differed widely, both with respect to data preprocessing and statistical modelling (that is, included covariates, link functions, assumption of hierarchical structure).
A precursor to the multiple-analysts approach concerns the 1857 ‘Cuneiform competition’, in which four scholars independently translated a previously unseen ancient Assyrian inscription61. The overlap between their translations—which were sent to the Royal Asian Society in sealed envelopes, and were simultaneously opened and inspected by a separate committee of examiners—was striking and put to rest any doubts concerning the method used to decipher such inscriptions. The multiple-analysts approach never caught on in practice, although recent examples exist in psychology and neuroscience3,4,5,7,62,63.
As was the case for the construction of the data multiverse and the model multiverse, a pragmatic limitation of the multiple-analyst approach lies in the extra work that it entails, specifically with respect to (1) finding knowledgeable analysts who are interested in participating; (2) documenting the dataset, describing the research question and identifying the target of statistical inference; (3) collating the initial responses from each team and potentially coordinating a review and feedback round. Although differences in opinion should be respected, there needs to be ways to filter out analysis approaches that involve clear mistakes. An additional limitation concerns possible homogeneity of the analysts. For example, all analysts involved could be rigidly educated in the same school of thought, share cultural or social biases or just make the same mistake. In such a case, the results may create an inflated sense of certainty in the conclusion that was reached. These potential limitations can be mitigated by selecting a diverse group of analysts and incorporating feedback and revision options in the process7, a round-table discussion5 or, more systematically, a Delphi approach64.
There are no explicit guidelines concerning the multiple-analysts approach. We propose that the optimal number of analysts to be included depends on factors such as the complexity of the data, the importance of the research question (that is, a clinical trial on the effectiveness of a new drug against coronavirus disease 2019 (COVID-19) warrants a relatively large number of analysts) and the probability that the analysts could reasonably reach a different conclusion (that is, there may be multiple ways to interpret the research question, and there may be multiple dependent variables and predictor variables that could or could not be relevant).
When analysts are selected, care should be taken to ensure heterogeneity, diversity and balance. Specifically, one should be mindful of the potential biasing effects of specific background knowledge, culture, education and career stage of the analyst.
The ASA Guidelines emphasize the legitimacy and value in alternative analytic approaches, stating that “[t]he practice of statistics requires consideration of the entire range of possible explanations for observed phenomena, and distinct observers […] can arrive at different and potentially diverging judgements about the plausibility of different explanations” (page 5 of ref. 33).
Interpreting results modestly
By modestly interpreting the results, the analyst explicitly acknowledges any remaining doubts concerning the importance, replicability and generalizability of the scientific claims at hand.
Benefits and example
Modestly presented scientific claims enable the reader to evaluate the outcomes for what they usually are: not final, but tentative results pointing in a certain direction, with considerable uncertainty surrounding their generalizability and scope. Overselling results might lead to the misallocation of public resources towards approaches that are in fact not properly validated and not ready for application in practice. Furthermore, researchers themselves risk losing long-term credibility for short-term gains of greater attention and higher citation counts. Moreover, after having publicly committed to a bold claim, it becomes difficult to admit that one’s initial assessment was wrong; in other words, overconfidence is not conducive to scientific learning.
Scientists of true modesty remain doubtful even at moments of great success. For example, when James Chadwick found experimental proof of neutrons, the discovery that earned him the Nobel prize, he communicated it modestly under the title ‘Possible existence of neutron’65.
Tukey66 already remarked that “Laying aside unethical practices, one of the most dangerous [[…] practices of data analysis […]] is the use of formal data-analytical procedures for sanctification, for the preservation of conclusions from all criticism, for the granting of an imprimatur.” (page 13 of ref. 66). Almost 60 years later, an editorial in Nature Human Behaviour warned its readers about “conclusive narratives that leave no room for ambiguity or for conflicting or inconclusive results” (page 1 of ref. 67). Similarly, Simons et al.68 suggested adding a mandatory ‘constraints on generality’ statement in the discussion section of all primary research articles in the field of psychology to prevent authors from making wildly exaggerated claims of generality. This suggests that scientific modesty is rarer than we would expect if Mertonian norms were widely adopted. There are some clear indications of a lack of modesty. First, the frequency of stronger language (words such as amazing, ground-breaking, unprecedented) seemed to have increased in the past few decades69. Second, dichotomization of findings (that is, ignoring the uncertainty inherent to statistical inference) is common practice (ref. 42, see paragraph 4.3). Third, textbooks (which are typically a reflection of current practice) on how to write papers often explicitly encourage authors to overclaim70,71.
Publications and grants are important for scientific survival. Coupled with the fact that journals and funders often prefer ground-breaking and unequivocal outcomes, it may be detrimental to one’s success to modestly interpret the results. The encouragement of this Mertonian practice may require change at an institutional level, although some have argued that scientists should not hide behind the system when defending their behaviour72.
There are several ways that we can contribute to increasing intellectual modesty. First, we could encourage intellectual modesty in the work of others when we act as reviewers of papers and grant proposals73. As a reviewer’s career is independent of how they evaluate a paper, they can make a positive review conditional on a more modest presentation of outcomes. Hoekstra and Vazire73 present a list of suggestions for increasing modesty in the traditional sections of an empirical article, which can be used by authors as well. One example includes “Titles should not state or imply stronger claims than are justified (that is, causal claims without strong evidence)” (page 16 of ref. 73) .
The ASA Guidelines also state: “[t]he ethical statistician is candid about any known or suspected limitations, defects, or biases in the data that may affect the integrity or reliability of the statistical analysis” (page 2 of ref. 33).
Sharing data and code
By sharing data and analysis code, researchers provide the basis for their scientific claims. Ideally, data and code should be shared publicly, freely and in a manner that facilitates reuse.
Benefits and example
As there are many different ways of processing and analysing data7,46, sharing code promotes reproducibility and encourages sensitivity analyses. Sharing data and code also enables other researchers to establish the validity of the original analyses, it can facilitate collaboration, but it can also serve as protection against data loss. When publishing his theory on general intelligence, Spearman74 shared his data as an appendix to the article. A century later, this act of foresight enabled scientists to use this dataset for both research and education. Because Spearman made his data publicly available, other researchers could establish the reproducibility and generalizability of the findings.
Data sharing has never been easier. Public repositories offer free storage space for research materials, data (that is, the Open Science Framework) and code (that is, GitHub). While data sharing is not yet a general practice in most scientific fields, several recent initiatives (that is, Open Data/Code/Materials badges75), standards (TOP Guidelines76), journals (that is, Scientific Data) and checklists (that is, Transparency Checklist77) are helping to promote this research practice. When sharing raw data is unfeasible, researchers can make aggregated data summaries available—for example, the data used to generate certain plots or covariance matrices of involved variables.
Restrictions imposed by funders, ethics review boards in universities and other institutions, collaborators and legal contracts may limit the extent to which data can be publicly shared. There may also be practical considerations (that is, sharing big data), data use agreements, privacy rights and institutional policies that can curtail sharing intentions. What remains central is to inform the readers about the accessibility of the data of the analysis. Note that these limitations should not apply to the analysis code provided that code is solely reflective of the researcher’s analysis actions and is free of any data privacy issues.
An important principle of sharing data is that they should be findable, accessible, interoperable and reusable (FAIR78). Several guides are available discussing the practical79 and ethical80 aspects of data sharing. Researchers should follow the data sharing procedures and requirements of their fields81,82 and indicate the accessibility of the data in the research report76,83. The ASA Ethical Guidelines33 for Statistical Practice state that the ethical statistician “[p]romotes sharing of data and methods as much as possible”, and “[m]akes documentation suitable for replicate analyses, metadata studies, and other research by qualified investigators.” (page 5 of ref. 33).
If the statistical literature is any guide, one may conclude that statisticians rarely agree with each other. For example, the 2019 special issue in The American Statistician featured 43 articles on P values and, in their editorial, Wasserstein et al.13 stated that “the voices in the 43 papers in this issue do not sing as one”. However, despite the continuing disagreements about the foundations of statistical inference, we believe that there is nevertheless much common ground among statisticians, specifically with respect to the ethical aspects of their profession. To examine this ethical dimension more systematically, we started by considering the Mertonian norms that characterize the ethos of science and outlined a non-exhaustive list of seven concrete, teachable, and implementable practices that we believe need wider propagation.
In essence, these practices are about promoting transparency and the open acknowledgement of uncertainty. With agreement on such practices explicitly acknowledged, we believe that commonly discussed contentious issues (that is, P values) may become less crucial. Indeed, in a letter to his frequentist nemesis Sir Ronald Fisher, the arch-Bayesian Sir Harold Jeffreys wrote “Your letter confirms my previous impression that it would only be once in a blue moon that we would disagree about the inference to be drawn in any particular case, and that in the exceptional cases we would both be a bit doubtful” (page 162 of ref. 84).
We hope that the proposed statistical practices will improve the quality of data analysis across the board, especially in applied disciplines that are perhaps unfamiliar with the ethical aspects of statistics, aspects that a statistician may take for granted. Furthermore, instead of counting on them to be absorbed through osmosis, we believe that it is important to include these ethical considerations—and their statistical consequences—explicitly in the statistics curricula. Statistical techniques other than those discussed here may also further the Mertonian ideals. We hope that this contribution provides the impetus for a deeper exploration of how data analysis in applied fields can become more transparent, more informative and more open about the uncertainties that inevitably arise in any statistical data analysis problem.
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We are grateful to N. Lazar for her comments on a draft version. We thank everyone who was involved in drafting the initial list of statistical procedures during the hackathon that took place at the 2019 meeting of the Society for the Improvement of Psychological Science in Rotterdam, The Netherlands. This work was supported in part by a European Research Council (ERC) grant to E.-J.W. (no. 283876), a Netherlands Organisation for Scientific Research (NWO) grant to A. Sarafoglou (no. 406-17-568), as well as a Dutch scientific organization Vidi grant from the NWO to D.v.R. (no. 016.Vidi.188.001).
The authors declare no competing interests.
Peer review information Nature Human Behaviour thanks David Hand and Ulf Toelch for their contribution to the peer review of this work.
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Wagenmakers, EJ., Sarafoglou, A., Aarts, S. et al. Seven steps toward more transparency in statistical practice. Nat Hum Behav 5, 1473–1480 (2021). https://doi.org/10.1038/s41562-021-01211-8
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