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From Wikipedia, the free encyclopedia. Auto-correcting computer data storage. Main article: Registered memory. Retrieved October 20, Swift and Steven M. Lay summary — ZDNet. Retrieved Ars Technica. Microsoft Research. Archived from the original on Retrieved 15 October Sadler and Daniel J. Primary computer data storage technologies.

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Categories : Computer memory Fault-tolerant computer systems. Hidden categories: Webarchive template wayback links All articles with dead external links Articles with dead external links from July Articles with permanently dead external links Articles with dead external links from September Articles with short description All articles with unsourced statements Articles with unsourced statements from November Articles with unsourced statements from August Articles containing potentially dated statements from All articles containing potentially dated statements.

Namespaces Article Talk. Views Read Edit View history. By using this site, you agree to the Terms of Use and Privacy Policy. Current DRAM e. First, researcher s could R5: Misreport results and p-values Bakker and Wicherts, , for instance by presenting a statistically non-significant result as being significant. This practice and similar practices of misreporting of results e. They can falsely present results of data explorations as though they were confirmatory tests of hypotheses that were stipulated in advance Wagenmakers et al.

Both types of misreporting lower trust in reported findings and potentially also the replicability of results in later research. We created a list of 34 researcher DFs, but our list is in no way exhaustive for the many choices that need be made during the different phases of a psychological experiment.

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Some of the researcher DFs are clearly related to others, but we nonetheless considered it valuable to list them separately according to the phase of the study. One can envision many other ways to create bias in studies, including poorly designed experiments with confounding factors, biased samples, invalid measurements, erroneous analyses, inappropriate scales, data dependencies that inflate significance levels, etc.

Moreover, some of the researcher DFs on our list do not apply to other statistical frameworks and our list does not include the specific DF associated with those frameworks e. Here we focused on the researcher DFs that are often relevant even for well-designed and rigorously conducted experiments and other types of psychological studies that use NHST to test their hypotheses of interest. What matters is that the data could be collected and analyzed in different ways and that the final analyses reported in the research article could have been chosen differently if the results based on these different choices and bearing on statistical significance had come out differently.

The issue, then, is not that all researchers try to obtain desirable results by exploiting researcher DFs but rather that the researcher DFs have strong potential to create bias. Such potential for bias is particularly severe for experiments that study subtle effects with relatively small samples. Hence, we need an appropriate way to deal with researcher DFs. One way to assess the relevance of choices is to report all potentially relevant analyses either as traditional sensitivity analyses or as a multiverse analysis Steegen et al.

Another solution is that the data are available for independent reanalysis after publication, although this is not always possible due to low sharing rates Wicherts et al. However, preventing bias is better than treating it after it has occurred. Thus, the preferred way to counter bias due to researcher DFs is to preregister the study in a way that no longer allows researchers to exploit them. The ideal preregistration of a study provides a specific , precise , and exhaustive story of the planned research, that is, it describes all steps, with only one interpretation, and excludes other possible steps.

Our list can be used in research methods education, as a checklist to assess the quality of preregistrations, and to determine the potential for bias due to arbitrary choices in unregistered studies. Presently, we are conducting a study focusing on the quality of a random sample of actual preregistrations on the Open Science Framework in which we use a scoring protocol based on our checklist to assess the degree to which these preregistrations avoid any potential p -hacking.

A score 3 is assigned if it is also exhaustive, i. By applying the protocol, authors can also score their own preregistration, enabling them to improve their preregistration, and reviewers of registered reports and registered studies can use the protocol as well. Both authors and reviewers can thus use the protocol to limit potential p -hacking in planned studies. We suggest a few avenues for future research. First, while most of the researcher DFs in our list are relevant to other statistical frameworks as well, the list should be adapted for studies planning to use confidence intervals and certain precision of effect size estimates Cumming, , ; Maxwell et al.

These transparent practices have many benefits and are currently gaining traction e.

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While we believe all these open practices strengthen research, a lot can still be gained by creating protocols that provide specific, precise, and exhaustive descriptions of materials, data, and workflow. The preparation of this article was supported by Grants , , and from the Netherlands Organization for Scientific Research NWO. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Asendorpf, J.

Recommendations for increasing replicability in psychology. Bakker, M. The rules of the game called psychological science. The mis reporting of statistical results in psychology journals. Methods 43, — Outlier removal, sum scores, and the inflation of the Type I error rate in independent samples t tests.

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The power of alternatives and recommendations. Methods 19, — Bargh, J. Reis and C.

Judd Cambridge: Cambridge University Press , — Barnett, V. Outliers in Statistical Data. Google Scholar. Chambers, C. Registered reports: a new publishing initiative at Cortex. Cortex 49, — Chan, A. Empirical evidence for selective reporting of outcomes in randomized trials - Comparison of Protocols to published articles. JAMA , — Cohen, J. Things I have learned thus far. Cooper, H. Finding the missing science: the fate of studies submitted for review by a human subjects committee.

Methods 2, — Cumming, G. New York, NY: Routledge. The new statistics: why and how. Acta Psychol. DeCoster, J. Opportunistic biases: their origins, effects, and an integrated solution. Eich, E. Business not as usual. Francis, G. Replication, statistical consistency, and publication bias. Franco, A. Underreporting in psychology experiments: evidence from a study registry. Gelman, A. The statistical crisis in science. Hubbard, R. Ioannidis, J.

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Why most published research findings are false. PLoS Med. Why most discovered true associations are inflated. Epidemiology 19, — John, L. Measuring the prevalence of questionable research practices with incentives for truth-telling. Kerr, N. HARKing: hypothesizing after the results are known. Kidwell, M. Badges to acknowledge open practices: a simple, low-cost, effective method for increasing transparency. PLoS Biol. Kirkham, J. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews.

BMJ c Kriegeskorte, N. Everything you never wanted to know about circular analysis, but were afraid to ask. Blood Flow Metab. Circular analysis in systems neuroscience: the dangers of double dipping. Kruschke, J. London: Academic Press. LeBel, E. Maxwell, S. Is psychology suffering from a replication crisis? Nieuwenhuis, S. Erroneous analyses of interactions in neuroscience: a problem of significance.

Nosek, B. Promoting an open research culture: author guidelines for journals could help to promote transparency, openness, and reproducibility. Science , — Scientific Utopia: II - Restructuring incentives and practices to promote truth over publishability. Nuijten, M. The prevalence of statistical reporting errors in psychology Methods doi: PubMed Abstract. Open Science Collaboration Estimating the reproducibility of psychological science.

Science aac Oppenheimer, D. Instructional manipulation checks: detecting satisficing to increase statistical power. Poldrack, R. Scanning the Horizon: towards transparent and reproducible neuroimaging research. Rosenthal, R. Experimenter Effects in Behavioral Research.

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Sala-I-Martin, X. I just ran two million regressions. Schafer, J. Missing data: our view of the state of the art. Methods 7, — Schaller, M. The empirical benefits of conceptual rigor: systematic articulation of conceptual hypotheses can reduce the risk of non-replicable results and facilitate novel discoveries too. Sedlmeier, P. Do studies of statistical power have an effect on the power of studies?

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Simmons, J. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Simons, D. An introduction to registered replication reports at perspectives on psychological science. Simonsohn, U. Correcting for publication bias using only significant results. Steegen, S. Increasing transparency through a multiverse analysis.

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