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Because Null Hypothesis Significance Tests (NHST) can be misleading particularly when sample sizes are large, reporting effect sizes is recommended. Effect sizes such as Cohen's d, however, are abstract statistics and not relevant to decision-making. This article introduced an alternative formulation of Cohen's d based on a "minimal important percent difference" (MIPD) between groups that has an intuitive, practical meaning. Cohen's d was calculated based on detecting MIPDs of 5% and 10% differences in means. Power analyses were used to determine the number of responses needed to detect differences in means. Power-probability sample size requirements were examined for an 80% and a 50% chance of accepting a difference of MIPD at p <.05. We proposed a four-step process to show how both effect size and NHST jointly contribute to decision-making. To demonstrate the process, data were obtained for 6 years of annual waterfowl surveys in Illinois (N = 17,265, average response rate = 41%). [ABSTRACT FROM AUTHOR]

Data literacy, which is considered among 21st century skills, is becoming increasingly important. In particular, incentives for data-driven decision-making draw attention to data literacy. Data literacy is also critical for teachers who need to use data in educational settings. Therefore, there is a need for data on whether teachers are equipped with data literacy competencies before their service. In this study, pre-service teachers' data literacy competencies were examined. The case study approach was used in the study, which was conducted as qualitative research. The study group consisted of 61 pre-service teachers studying in three different undergraduate programs. Data were collected through the "What do the data say?" activity instrument. Rubrics were used to analyze the data. The findings of the study showed that while there was no difference in terms of using data and data communication, there was a statistically significant difference between the programs in terms of data recognition, comparing data and establishing relationships between data competencies, as well as total data literacy. In addition, it was found that the majority of pre-service teachers were partially inadequate or inadequate in terms of using data, data communication and total data literacy. Nearly half of them were partially inadequate or inadequate in comparing data and establishing relationships between data. The results indicated that preservice teachers have certain deficiencies regarding data literacy. [ABSTRACT FROM AUTHOR]

Z-numbers contain fuzzy restrictions, credibility measures, and probability distributions to effectively represent uncertain information. Converting Z-numbers to fuzzy numbers facilitates extensive applications (such as multi-attribute decision-making (MADM)), thus becoming valuable for research purposes. Regarding Z-number conversions, the original method never considers the association probability, while probabilistic strategies offer better informatization. Recently, a probability-driven conversion starts with a linear transformation of the centroid difference between the fuzzy restriction and probabilistic distribution. However, it has the invalidation weakness of edge information due to underlying non-normalization. To improve this probability-linear conversion, a Z-number conversion is proposed by using underlying probability-exponential descriptions, and this new method is further applied to MADM. At first, the current probability-linear conversion is analyzed based on the initial non-probabilistic conversion, and its intrinsic weakness and correctional improvement are revealed. Then, the novel probability-exponential conversion resorts to an exponential characterization of centroid difference between the restriction and distribution, and it gains information enrichment due to underlying normalization. The refined method preserves the inherent characteristics of Z-numbers more effectively, facilitating their application in subsequent engineering practices. This is especially pertinent in decision-making systems based on expert input and initial value problems. The proposed method for converting Z-numbers aims to minimize information loss in transitions between Z-numbers and classical fuzzy numbers. This approach will be further explored in future research. Furthermore, the probability-exponential conversion induces an ExpTODIM algorithm for MADM, called PE-ExpTODIM. Three Z-number conversions (i.e., the non-probabilistic, probability-linear, and probability-exponential types) and three decision algorithms (i.e., ExpTODIM, EDAS, MOORA) are combined to establish a 3 × 3 framework of Z-number-driven MADM. Finally, the systematical 9 algorithms are applied to the problem of site selection of carbon storage. They are validated by criss-cross contrast analyses and statistical significance tests. Thus, PE-ExpTODIM exhibits the desired optimization. The last technology of statistical testing is original, ingenious, and valuable for MADM. [ABSTRACT FROM AUTHOR]

The probability of test object compliance with the specified requirements and non-binary decision is a measure of how likely the test object is to meet the criteria for acceptance or rejection based on multiple factors. It is necessary to calculate by using a valid mathematical model that takes into account the test object's characteristics, the test conditions, the test criteria, and the uncertainty of the measurements. The significance of the probability of test object compliance with the specified requirements and non-binary decision can help to evaluate the quality, reliability, and performance of the test object, as well as to support decision making in complex situations. [ABSTRACT FROM AUTHOR]

Patient-physician communication has the potential to improve outcomes and satisfaction through the shared decision-making process (SDM). This study aims to assess the relationship between perception of SDM and demographic, clinical, and patient-reported outcomes in patients undergoing Hallux Valgus (HV) correction. A prospective analysis of 306 patients scheduled for HV surgery was completed. The CollaboRATE score was used to measure SDM. Multivariable linear regression model was used to assess whether SDM scores were associated with preoperative characteristics or postoperative outcome scores. The mean CollaboRATE score was 2.9 (SD 0.9) and did not differ by age, socioeconomic status, or sex. Lower CollaboRATE scores were associated with more symptoms of depression, lower socioeconomic status, and lower general health scores (p-value < 0.05). There was no association between SDM scores and postoperative outcome scores. In this study, patients with depressive symptoms and lower socioeconomic status had worse perceptions of SDM. There was no difference in postoperative outcomes among participants based on SDM scores. Level of Evidence: Level III, prospective observational study [ABSTRACT FROM AUTHOR]

The use of Monte‐Carlo (MC) p$$ p $$‐values when testing the significance of a large number of hypotheses is now commonplace. In large‐scale hypothesis testing, we will typically encounter at least somep$$ p $$‐values near the threshold of significance, which require a larger number of MC replicates than p$$ p $$‐values that are far from the threshold. As a result, some incorrect conclusions can be reached due to MC error alone; for hypotheses near the threshold, even a very large number (eg, 106$$ 1{0}^6 $$) of MC replicates may not be enough to guarantee conclusions reached using MC p$$ p $$‐values. Gandy and Hahn (GH)6‐8 have developed the only method that directly addresses this problem. They defined a Monte‐Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MC p$$ p $$‐values are different from decisions based on idealp$$ p $$‐values; their method then makes decisions by controlling the MCER. Unfortunately, the GH method is frequently very conservative, often making no rejections at all and leaving a large number of hypotheses "undecided". In this article, we propose MERIT, a method for large‐scale MC hypothesis testing that also controls the MCER but is more statistically efficient than the GH method. Through extensive simulation studies, we demonstrate that MERIT controls the MCER while making more decisions that agree with the ideal p$$ p $$‐values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study. [ABSTRACT FROM AUTHOR]