Yet the report also became a magnet for critics, with many questioning the study's baseline assumptions, sampling methods, and data reporting procedures [ 3- 6].
Using a Power and Sample Size Calculations Programme version 3.0.14 [ 21] and based on the above assumption, sample size for the intervention (children in DMP schools) and control group (children without DMP), with 80% power and 5% Type 1 error, was 1100.
Although most algorithms are robust to minor violations of this assumption, sampling bias in the case of genetic datasets may be too large for algorithms to accurately recover stratification.
Based on these assumptions, the sample size was calculated as follows: This yields a sample size of 384.
On the basis of these assumptions, a sample size of 29 dairy farms and a sub-sample of 1077 milking cows was calculated.
Based on these assumptions, the sample size for the study was computed using a one-sample population proportion formula.
Coefficients (β's) were estimated with ordinary least squares, based on two assumptions: the sample is random, and the error term is of zero conditional mean and homoskedasticity.
Assumptions of sample size were based on on data provided by Pearson et al, Summar et al and Moonen et al [18], [22], [24].
Under these assumptions, a sample size of 150 patients was required.
Based on these assumptions, a sample size of 20 evaluable subjects per group was required.
This method makes no assumptions for sample distribution or measurement errors.
