Barely Significant
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“may be significant”

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In the literature

studied patients with a BMI > 40 kg/m 2 and found that NC was a significant predictor of difficult intubation.[ 13 ] For a circumference of 40 cm, the probability of a difficult intubation was approximately 5%, while the probability increased up to 35% at a NC of 60 cm.[ 13 ] The odds increased by 1.13 times when NC increased by 1 cm.[ 13 ] Yet, it is not only the circumference but also the amount of pre-tracheal soft tissue that matters, as demonstrated in obese patients by the use of ultrasound.[ 30 ] Furthermore, gender-related anatomic differences may be significant; in our study, men had a significantly larger neck circumference than women, and similar findings have been reported by Brodsky et al .[ 13 ] Moreover, in our study, the predictive accuracy of NC was significantly improved in women when a different, gender-specific cut-off point was used.
treatment costs and illness, is there evidence to support this view and if yes, whose influence better explains the possibility of end results of treatment, empirically? RQ3: Can the likelihood of paying too little or too much out-of-pocket “extra thank-you money” be determined by the severity of illness and/or income of patients? This OOP amount may be significant but if a patient appreciates the value of service, he/she would be willing to pay depending on his/her availability of finance, before or after the course of treatment.
For example, if 3 independent studies were to show a similar substantial improvement in patient outcome, but the p value in each study was p ≥ 0.05, the individual studies would be reported as demonstrating “no significant effect.” However, if the 3 studies were to be combined using meta-analysis, their combined p value may be significant.
This indicates that while population-level alcohol use in Alberta may not have statistically significantly increased, there may be significant increases in alcohol use among a subset of the population that might not have been captured by the outcome used to measure low-risk drinking in the present study.
31 , 32 When interpreting clinical measures, it is important to consider that, although small changes may be significant statistically, they may not be relevant clinically. 31 , 33 Numerous methods to derive the MCID have been described. 31 , 32 , 34 – 44 These include anchor-based methods, 31 , 36 , 39 , 40 which compare a patient's change score with another measure of clinically relevant change, and distribution-based methods, 36 , 41 – 43 such as the standard error of measurement (SEM), effect size, and one third the standard deviation (SD) at baseline, which are built on the statistical distribution and psychometric properties of the measure in a population.