Our second research question examined to what degree sample size influences SIDE scoring model performance.
To what degree does sample size impact computer-scoring success?
For example, to detect a 5 degree difference sample size would a sample size of over 120 participants.
Chi-square accompanied by degrees of freedom, sample size, p-value and the root mean square error of approximation (RMSEA) were used as indices of absolute fit between the models and the data.
Analysis by country would have reduced this effect to some degree, but sample sizes were generally small at the country level, limiting the interpretability of findings.
While the findings are to some degree limited by sample size and by the nested approach adopted, they demonstrate that the extraction methods used in genetic studies of fixed tissues need to be chosen carefully, in light of the specific endpoints of particular investigations.
Numbers in parentheses refer to degrees of freedom and sample size, respectively.
We then use two large, open-access data sets to empirically estimate false positive rates and how these depend on sample size, degree of smoothing and modulation.
Our review and investigation provide three main results: (i) when groups of equal size are compared false positive rate is not higher than expected, i.e. about 5%; (ii) the sample size, degree of smoothing and modulation do not appear to influence false positive rate; (iii) when they exist, false positive findings are randomly distributed across the brain.
Although this systematic review provided evidence that physical exercise has positive effects on frail older adults, we should consider that there was great variety among the studies concerning sample size, degree of frailty, types of interventions and types of assessments.
