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We ask whether graphs of mean values distort effect size interpretations, producing inflation and high variability, relative to sina plots, and/or relative to no graph at all.

We ask whether graphs of mean values distort effect size interpretations, producing inflation and high variability, relative to sina plots, and/or relative to no graph at all.

We ask if graphs of mean values distort effect size interpretations, producing inflation and high variability, relative to sina plots, and/or relative to no graph at all.

Human behavioral data are frequently communicated via bar graphs of mean values. Such “mean bar graphs” are presumed to communicate empirical results effectively to non-experts. Yet direct evidence for or against this presumption remains sparse. Here, we ask how a set of widely-consumed scientific mean bar graphs are interpreted by a demographically diverse sample of 133 participants. We use four mean bar graphs of research results, taken from major introductory psychology textbooks, which vary in content (developmental, clinical, social, cognitive), form (unidirectional bars, bidirectional bars), visual aesthetics (four different textbooks’ look and feel), data type (objective performance, survey ratings), and study design (experimental, non-experimental). Participants created a detailed sketch of each graph, adding datapoints for their best guess of individual values that were averaged to produce the mean values. Drawn data values were then analyzed as if they were real data. Results were examined for deviations from the ground truth of the published data and for variability between participants. On three separate dimensions—location of the mean, variability around the mean, and normality of distribution shape—we found large, systematic deviations from ground truth and high inter-participant variability. Together, the combination of systematic deviations and inter-participant variability yielded common, extreme misunderstandings, or fallacies, on all three dimensions. We call these fallacies: (1) the Bar-Tip Limit Error: most or all data plotted inside the bar, as if the bar’s tip represented the outside limit of the data rather than its balanced center point; (2) the Dichotomization Fallacy: little to no overlap between distributions that should show substantial overlap; (3) the Uniformity Fallacy: data distributed uniformly over its entire range, absent the tails that were present in the real data. These results replicated across the four varying stimulus graphs, suggesting that they are not limited to specific graph form, content, visual aesthetic, data type, or study design. We conclude that the choice to communicate human behavioral data via a mean bar graph carries with it at least two major risks. First, different viewers may walk away from the same graph with widely divergent interpretations of the presented evidence. Second, interpretations may deviate systematically, and, for many viewers, to an extreme degree, from ground truth.

Comparing Dynamic and Static Flanker task response times for effect size and task reliability.

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r

SummaryRevealing the contributions of genes to plant phenotype is frequently challenging because the effects of loss of gene function may be subtle or be masked by genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana.An image segmentation-based (ImageJ) and a Faster Region Based Convolutional Neural Network (R-CNN) approach were used for measuring two Arabidopsis fitness traits: seed and fruit counts.Although straightforward to use, ImageJ was error-prone (correlation between true and predicted seed counts, r2=0.849) because seeds touching each other were undercounted. In contrast, Faster R-CNN yielded near perfect seed counts (r2=0.9996) and highly accurate fruit counts (r2=0.980).By examining seed counts, we were able to reveal fitness effects for genes that were previously reported to have no or condition-specific loss-of-function phenotypes.Our study provides models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits in the study of gene functions.

Diagnosis of Atrial Fibrillation (AF) after stroke is a national priority. AF-related strokes typically cost twice as much to manage as non-AF related strokes (Miller et al, 2005). If diagnosed with AF, further stroke can be reduced by 60% by taking medications such as anticoagulants. Because AF can be paroxysmal- that is the heart can go in and out of AF- it is not always detected during hospitalisation for stroke. If there is a reasonable suspicion that stroke may have resulted from undetected AF, guidelines recommend screening with a Holter monitor - an ambulatory electrocardiogram (ECG) applied in a hospital clinic for 72 hours. A ‘Test of Change’ within one local hospital in Scotland (NHS Lanarkshire) was conducted during 2019 where a new managed service involving devices (applied to n=64 patients) were considered for AF screening that would allow for up to 14 days of continuous monitoring in the home setting. This new technology enabled service also included reporting conducted by the company providing the service as a proposed alternative to clinicians inside the NHS doing all the report reading and analysis. Clinicians and other key implementers involved in the planning, set up, delivery, and evaluation of this service innovation were interviewed throughout the project to gather qualitative data on the potential barriers and facilitators to this type of service working longer term locally for them and or potentially scaling nationally. Patient experience was also captured through interviews with a sample of patients who received the new service (8/64; 12.5%) to capture acceptability of the new device and resulting service within this NHS setting. Findings revealed that patients found the new monitoring device acceptable. Stroke clinicians and nurses invested additional time in training (4 half days) to use the software, apply the devices and in home-visits to apply devices but also highly valued the availability of 14 days of continuous data. For further adoption of this type of service, it is critical to explore whether clinicians would adopt a system or service that produces the reports for them or whether they would prefer to have the reports generated by NHS staff (trust and control versus cost and capacity for reporting). It is anticipated that this report will benefit policy makers in government, operational managers, clinical leads, service managers and digital and IT managers and leads and many others by providing key barriers and facilitators and therefore a realistic checklist of what must be considered in planning for implementation of new technology enabled services to monitor for intermittent AF within a care pathway.

Myeloproliferative neoplasms (MPNs) are a heterogeneous group of diseases including polycythemia vera (PV), essential thrombocythemia (ET), and primary(idiopathic) myelofibrosis (PMF). In this systematic review, we provide a comprehensive report on the incidence and prevalence of MPNs across the globe. Electronic databases (PubMed, EMBASE, MEDLINE, and Web of Science) were searched from their inception to August 2012 for articles reporting MPN incidence or prevalence rates. A random effects meta-analysis was undertaken to produce combined incidence rates for PV, ET, and PMF. Both heterogeneity and small study bias were assessed. Thirty-four studies were included. Reported annual incidence rates ranged from 0.01 to 2.61, 0.21 to 2.27, and 0.22 to 0.99 per 100,000 for PV, ET, and PMF, respectively. The combined annual incidence rates for PV, ET, and PMF were 0.84, 1.03, and 0.47 per 100,000. There was high heterogeneity across disease entities (I(2) 97.1-99.8%) and evidence of publication bias for ET and PMF (Egger test, P = 50.007 and P ≤ 0.001, respectively).The pooled incidence reflects the rarity of MPNs. The calculated pooled incidence rates do not reflect MPN incidence across the globe due to the high unexplained heterogeneity. Improved, widespread registration of MPNs would provide better information for global comparison of the incidence and prevalence of MPNs.

Myeloproliferative neoplasms (MPNs) are a heterogeneous group of diseases including polycythemia vera (PV), essential thrombocythemia (ET), and primary(idiopathic) myelofibrosis (PMF). In this systematic review, we provide a comprehensive report on the incidence and prevalence of MPNs across the globe. Electronic databases (PubMed, EMBASE, MEDLINE, and Web of Science) were searched from their inception to August 2012 for articles reporting MPN incidence or prevalence rates. A random effects meta-analysis was undertaken to produce combined incidence rates for PV, ET, and PMF. Both heterogeneity and small study bias were assessed. Thirty-four studies were included. Reported annual incidence rates ranged from 0.01 to 2.61, 0.21 to 2.27, and 0.22 to 0.99 per 100,000 for PV, ET, and PMF, respectively. The combined annual incidence rates for PV, ET, and PMF were 0.84, 1.03, and 0.47 per 100,000. There was high heterogeneity across disease entities (I(2) 97.1-99.8%) and evidence of publication bias for ET and PMF (Egger test, P = 50.007 and P ≤ 0.001, respectively).The pooled incidence reflects the rarity of MPNs. The calculated pooled incidence rates do not reflect MPN incidence across the globe due to the high unexplained heterogeneity. Improved, widespread registration of MPNs would provide better information for global comparison of the incidence and prevalence of MPNs.