4 Matching Annotations
- Sep 2018
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www.nature.com www.nature.com
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In stage 1, we find a significant effect in the same direction as the original study for 12 replications16,17,18,19,22,23,24,25,27,29,30,36 (57.1%) (Fig. 1a and Supplementary Table 3). When we increase the statistical power further in stage 2 (Fig. 1b and Supplementary Table 4), two additional studies20,31 replicate based on this criterion. By mistake, a second data collection was carried out for one study16 replicating in stage 1; thus, we also include this study in the stage 2 results to base our results on all the data collected. This study16 does not replicate in stage 2. This may suggest that replication studies should routinely be powered to detect at least 50% of the original effect size or that one should use a lower P value threshold than 0.05 for not continuing to stage 2 in our two-stage testing procedure. Based on all of the data collected, 13 (61.9%) studies replicated after stage 2 using the statistical significance criterion.
Main result of the study
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To complement these indicators, we present results for: (1) a meta-analytic estimate of the original and the replication results combined12, (2) 95% prediction intervals47, (3) the ‘small telescopes’ approach46, (4) the one-sided default Bayes factor48, (5) a Bayesian mixture model49, and (6) peer beliefs about replicability50.
These are some interesting ways of assessing replicability of hypothesis driven results based on statistical significance test.
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pre-registered at the Open Science Framework (OSF) and sent to the original authors for feedback and verification prior to data collection (the pre-replication versions of the replication reports and the final versions are posted at the project’s OSF repository (https://osf.io/pfdyw/)
This is a great way to state the hypothesis and clarify the research project before moving format. Gives a clear indication of what we plan to try, how it will be evaluated and weather it failed or succeeded.
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- Aug 2015
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cs231n.github.io cs231n.github.io
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This is a really useful visualization of the Multi Class SVM loss
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