Most parametric tests start with the basic assumption on the distribution of populations. The conditions required to conduct the t-test include the measured values in ratio scale or interval scale, simple random extraction, normal distribution of data, appropriate sample size, and homogeneity of variance. The normality test is a kind of hypothesis test which has Type I and II errors, similar to the other hypothesis tests. It means that the sample size must influence the power of the normality test and its reliability. It is hard to find an established sample size for satisfying the power of the normality test. In the current article, the relationships between normality, power, and sample size were discussed. As the sample size decreased in the normality test, sufficient power was not guaranteed even with the same significance level. In the independent t-test, the change in power according to sample size and sample size ratio between groups was observed. When the sample size of one group was fixed and that of another group increased, power increased to some extent. However, it was not more efficient than increasing the sample sizes of both groups equally. To ensure the power in the normality test, sufficient sample size is required. The power is maximized when the sample size ratio between two groups is 1 : 1.

Is the primary endpoint clearly defined?

Background: Postoperative nausea and vomiting (PONV) refers to nausea and vomiting that occurs within 24-h after surgery or in the post-anesthesia care unit (PACU). Previous studies have reported that the use of remimazolam, a newer benzodiazepine (BDZ) hypnotic, for anesthesia results in less PONV. In this study, we compared the rate of PONV between sevoflurane and remimazolam after general anesthesia.Methods: In this prospective randomized controlled trial, participants aged 20–80 years who underwent elective laparoscopic cholecystectomy or hemicolectomy were randomized to either the remimazolam or sevoflurane group. The primary outcome was PONV incidence for 24-h after surgery. Secondary outcomes comprised of PONV at 30-min post-surgery, postoperative additional antiemetic use, and Quality of Recovery-15 (QOR-15) score at 24-h postoperatively.Results: Forty patients were enrolled in the study. The remimazolam group exhibited significantly lower rates of PONV for 24-h after surgery than did the sevoflurane group (remimazolam group vs. sevoflurane group; 5% vs. 45%, P = 0.003, respectively). The use of dexamethasone, a rescue antiemetic administered within 24 h of surgery, was substantially lower in the remimazolam group than in the sevoflurane group (0% in remimazolam vs. 30% in sevoflurane, P = 0.020). The QOR-15 score at 24-h after surgery showed no significant difference between the two groups.Conclusions: Compared to sevoflurane, opting for remimazolam as an intraoperative hypnotic may decrease the incidence of PONV and reduce antiemetic use for 24 h after laparoscopic surgery.

Tables and figures are commonly adopted methods for presenting specific data or statistical analysis results. Figures can be used to display characteristics and distributions of data, allowing for intuitive understanding through visualization and thus making it easier to interpret the statistical results. To maximize the positive aspects of figure presentation and increase the accuracy of the content, in this article, the authors will describe how to choose an appropriate figure type and the necessary components to include. Additionally, this article includes examples of figures that are commonly used in research and their essential components using virtual data.

Background: The prone position is used for a variety of procedures and surgeries, and hypotension is a commonly encountered complication. Case: A 75-year-old obese woman with lumbar spinal stenosis underwent posterior lumbar spinal fusion and posterolateral interbody fusion under general anesthesia. Before the surgery, when she was positioned on a Jackson spinal table in the prone position, sudden severe hypotension and hemodynamic collapse developed. The circulatory collapse was refractory to intravascular volume expansion and administration of inotropes and vasopressors. However, the application of external abdominal support immediately restored hemodynamic stability. The patient successfully underwent the surgery using an external abdominal support, and no post-operative complication was noted, including abdominal compartment syndrome. Conclusions: The Jackson spinal table allowed the abdomen to hang freely, providing abdominal decompression while resulting in a severely sagged abdomen. We suspected that the sagging abdomen had pulled the abdominal contents downwards, kinking the inferior vena cava or causing a venous pool in the abdomen, resulting in the obstruction of venous return to the heart.

Repeated measures analysis of variance (RM-ANOVA) is a specialized form of analysis of variance used for analyzing data involving repeated measurements, such as longitudinal data commonly encountered in anesthesia and pain medicine research. When data are collected at multiple time points across more than one group, RM-ANOVA evaluates the between-subject (group) effect, within-subject (time) effect, and interaction between these two factors. The group-by-time interaction effect indicates whether changes in an outcome variable over the study period differ among groups. Analyzing interaction contrasts between specific time points is particularly useful for identifying intervals where this interaction effect is significant. For instance, if an outcome variable is measured at multiple time points in two groups, the interaction contrast for any two time points represents the difference between the change in the outcome variable over that interval in one group and the corresponding change in the other group. An independent two-sample Student's t-test can then compare these differences to determine the statistical significance of the group-by-time interaction for the selected time points. Thus, incorporating interaction contrast analysis into RM-ANOVA enhances the interpretation of longitudinal data by pinpointing specific time intervals where significant interactions occur.