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In keeping with this year\'s focus on how we might foster a culture of research that values and consistently adopts optimal statistical practices, this column entry highlights practices our applied researchers can take up that may help remedy the gap between recommended statistical practices and implementation. This installment specifically encourages increasing the transparency of analyses, teaming up with colleagues with quantitative expertise, and disseminating resources that highlight optimal practices. [J Nurs Educ. 2024;63(7):490-491.].

From a statistical standpoint, individual cells are typically not independent experimental replicates. To test for differences in mean, cells from each experimental sample can be averaged and each sample\'s average treated as an n of 1. Here, I outline how to determine how many cells to average per sample.

BACKGROUND: Among Chinese medical students, medical statistics is often perceived as a formidable subject. While existing research has explored the attitudes of Chinese postgraduate medical students towards statistics and its impact on academic performance, there is a scarcity of studies examining the attitudes of Chinese medical undergraduates on this subject. This study endeavors to scrutinize the attitudes of Chinese medical undergraduates towards statistics, assessing their ramifications on learning achievements, and delving into the influence of demographic factors.
METHODS: 1266 medical undergraduates participated in this study, completing a questionnaire that included SATS-36 and additional queries. Furthermore, an examination was administered at the end of the medical statistics course. The analysis encompassed the SATS score and exam scores, examining both the overall participant population and specific demographic subgroups.
RESULTS: Undergraduate medical students generally exhibit a favorable disposition towards statistics concerning Affect, Cognitive Competence, and Value components, yet harbor less favorable sentiments regarding the Difficulty component of SATS-36, aligning with previous research findings. In comparison to their postgraduate counterparts, undergraduates display heightened enthusiasm for medical statistics. However, they demonstrate a lower cognitive capacity in statistics and tend to underestimate both the value and difficulty of learning statistics. Despite these disparities, undergraduate medical students express a genuine interest in statistics and exhibit a strong dedication to mastering the subject. It is noteworthy that students\' attitudes toward statistics may be influenced by their major and gender. Additionally, there exists a statistically significant positive correlation between learning achievement and the Affect, Cognitive Competence, Value, Interest, and Effort components of the SATS-36, while a negative correlation is observed with the Difficulty component.
CONCLUSIONS: Educators should carefully consider the influence of attitudes toward statistics, especially the variations observed among majors and genders when formulating strategies and curricula to enhance medical statistics education.

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BACKGROUND: Knowledge of statistics is highly important for research scholars, as they are expected to submit a thesis based on original research as part of a PhD program. As statistics play a major role in the analysis and interpretation of scientific data, intensive training at the beginning of a PhD programme is essential. PhD coursework is mandatory in universities and higher education institutes in India. This study aimed to compare the scores of knowledge in statistics and attitudes towards statistics among the research scholars of an institute of medical higher education in South India at different time points of their PhD (i.e., before, soon after and 2-3 years after the coursework) to determine whether intensive training programs such as PhD coursework can change their knowledge or attitudes toward statistics.
METHODS: One hundred and thirty research scholars who had completed PhD coursework in the last three years were invited by e-mail to be part of the study. Knowledge and attitudes toward statistics before and soon after the coursework were already assessed as part of the coursework module. Knowledge and attitudes towards statistics 2-3 years after the coursework were assessed using Google forms. Participation was voluntary, and informed consent was also sought.
RESULTS: Knowledge and attitude scores improved significantly subsequent to the coursework (i.e., soon after, percentage of change: 77%, 43% respectively). However, there was significant reduction in knowledge and attitude scores 2-3 years after coursework compared to the scores soon after coursework; knowledge and attitude scores have decreased by 10%, 37% respectively.
CONCLUSIONS: The study concluded that the coursework program was beneficial for improving research scholars\' knowledge and attitudes toward statistics. A refresher program 2-3 years after the coursework would greatly benefit the research scholars. Statistics educators must be empathetic to understanding scholars\' anxiety and attitudes toward statistics and its influence on learning outcomes.

The .05 boundary within Null Hypothesis Statistical Testing (NHST) \"has made a lot of people very angry and been widely regarded as a bad move\" (to quote Douglas Adams). Here, we move past meta-scientific arguments and ask an empirical question: What is the psychological standing of the .05 boundary for statistical significance? We find that graduate students in the psychological sciences show a boundary effect when relating p-values across .05. We propose this psychological boundary is learned through statistical training in NHST and reading a scientific literature replete with \"statistical significance\". Consistent with this proposal, undergraduates do not show the same sensitivity to the .05 boundary. Additionally, the size of a graduate student\'s boundary effect is not associated with their explicit endorsement of questionable research practices. These findings suggest that training creates distortions in initial processing of p-values, but these might be dampened through scientific processes operating over longer timescales.

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

BACKGROUND: This study aimed to evaluate plasma relaxin‑2 (RLN-2) levels in patients with arterial hypertension (AH) and their relationships with clinical and laboratory parameters.
METHODS: The study involved 106 hypertensive patients, including 55 with type 2 diabetes mellitus (T2DM), and 30 control subjects. Plasma RLN-2 levels were measured using an enzyme-linked immunosorbent assay kit.
RESULTS: RLN-2 levels were reduced in patients with AH compared to healthy volunteers (p < 0.001), and hypertensive patients with T2DM had lower RLN-2 levels than those without impaired glucose metabolism (p < 0.001). RLN‑2 was negatively correlated with systolic blood pressure (SBP) (p < 0.001) and anthropometric parameters such as body mass index (BMI; p = 0.027), neck (p = 0.045) and waist (p = 0.003) circumferences, and waist-to-hip ratio (p = 0.011). RLN‑2 also had inverse associations with uric acid levels (p = 0.019) and lipid profile parameters, particularly triglycerides (p < 0.001) and non-HDL-C/HDL‑C (p < 0.001), and a positive relationship with HDL‑C (p < 0.001). RLN‑2 was negatively associated with glucose (p < 0.001), insulin (p = 0.043), HbA1c (p < 0.001), and HOMA-IR index (p < 0.001). Univariate binary logistic regression identified RLN‑2 as a significant predictor of impaired glucose metabolism (p < 0.001).
CONCLUSIONS: Decreased RLN-2 levels in patients with AH and T2DM and established relationships of RLN‑2 with SBP and parameters of glucose metabolism and lipid profile suggest a diagnostic role of RLN‑2 as a biomarker for AH with T2DM.
UNASSIGNED: HINTERGRUND: Ziel der vorliegenden Studie war es, die Konzentrationen von Plasma-Relaxin‑2 (RLN-2) bei Patienten mit arterieller Hypertonie (AH) und ihre Beziehung zu klinischen und labormedizinischen Parametern zu untersuchen.
METHODS: An der Studie nahmen 106 AH-Patienten teil, davon 55 mit Diabetes mellitus Typ 2 (Typ-2-Diabetes) und 30 Kontrollpersonen. Die Plasmaspiegel von RLN‑2 wurden mit einem Enzymimmunoassay gemessen.
UNASSIGNED: Die RLN-2-Werte waren bei AH-Patienten im Vergleich zu gesunden Probanden erniedrigt (p < 0,001), und AH-Patienten mit Typ-2-Diabetes wiesen niedrigere RLN-2-Werte auf als Patienten ohne gestörten Glukosestoffwechsel (p < 0,001). RLN‑2 war negativ korreliert mit dem systolischen Blutdruck (SBP; p < 0,001) und mit anthropometrischen Parametern wie Body-Mass-Index (BMI; p = 0,027), Halsumfang (p = 0,045), Taillenumfang (p = 0,003) und Taille-Hüfte-Verhältnis (p = 0,011). Für RLN‑2 bestand auch ein inverser Zusammenhang mit dem Harnsäurespiegel (p = 0,019) und den Lipidprofilparametern, insbesondere mit Triglyzeriden (p < 0,001) und Nicht-High-Density-Lipoprotein-Cholesterin (Non-HDL-C)/HDL‑C (p < 0,001), außerdem lag eine positive Korrelation mit HDL‑C vor (p < 0,001). RLN‑2 war negativ mit Glukose (p < 0,001), Insulin (p = 0,043), HbA1c (p < 0,001) und dem HOMA-IR-Index (Homeostatic Model Assessment for Insulin Resistance; p < 0,001) assoziiert. In der univariaten binären logistischen Regression erwies sich RLN‑2 als signifikanter Prädiktor für einen gestörten Glukosestoffwechsel (p < 0,001).
UNASSIGNED: Erniedrigte RLN-2-Werte bei Patienten mit AH und Typ-2-Diabetes und nachgewiesene Beziehungen zwischen RLN‑2 und SBP sowie Parametern des Glukosestoffwechsels und des Lipidprofils deuten auf eine diagnostische Rolle von RLN‑2 als Biomarker für AH mit Typ-2-Diabetes hin.

BACKGROUND: Statisticians are fundamental in ensuring clinical research, including clinical trials, are conducted with quality, transparency, reproducibility and integrity. Good Clinical Practice (GCP) is an international quality standard for the conduct of clinical trials research. Statisticians are required to undertake training on GCP but existing training is generic and, crucially, does not cover statistical activities. This results in statisticians undertaking training mostly unrelated to their role and variation in awareness and implementation of relevant regulatory requirements with regards to statistical conduct. The need for role-relevant training is recognised by the UK NHS Health Research Authority and the Medicines and Healthcare products Regulatory Agency (MHRA).
METHODS: The Good Statistical Practice (GCP for Statisticians) project was instigated by the UK Clinical Research Collaboration (UKCRC) Registered Clinical Trials Unit (CTU) Statisticians Operational Group and funded by the National Institute for Health and Care Research (NIHR), to develop materials to enable role-specific GCP training tailored to statisticians. Review of current GCP training was undertaken by survey. Development of training materials were based on MHRA GCP. Critical review and piloting was conducted with UKCRC CTU and NIHR researchers with comment from MHRA. Final review was conducted through the UKCRC CTU Statistics group.
RESULTS: The survey confirmed the need and desire for the development of dedicated GCP training for statisticians. An accessible, comprehensive, piloted training package was developed tailored to statisticians working in clinical research, particularly the clinical trials arena. The training materials cover legislation and guidance for best practice across all clinical trial processes with statistical involvement, including exercises and real-life scenarios to bridge the gap between theory and practice. Comprehensive feedback was incorporated. The training materials are freely available for national and international adoption.
CONCLUSIONS: All research staff should have training in GCP yet the training undertaken by most academic statisticians does not cover activities related to their role. The Good Statistical Practice (GCP for Statisticians) project has developed and extensively piloted new, role-specific, comprehensive, accessible GCP training tailored to statisticians working in clinical research, particularly the clinical trials arena. This role-specific training will encourage best practice, leading to transparent and reproducible statistical activity, as required by regulatory authorities and funders.