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In addition to the above problems, African data have faced sampling (inappropriate sample size and sampling technique) and non-sampling error (respondent error, non response, recording error, etc).
Instead, we deliver a monster uppercut to statistically insignificant outliers and viciously decapitate datasets of inappropriate sample size.
11 12 In addition, erroneous MIDs may lead to inappropriate sample size calculations.
In association studies, contradictory conclusions may arise from population stratification or inappropriate sample size.
Many, however, have a suboptimal design, using a hospital-based population or having an inappropriate sample size.
Potential reasons for lack of statistical significance may be inappropriate sample size or premature reporting of ongoing studies – both should rightly decrease the chances for acceptance.
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It is well known that one of the main limitations and sources of confounding the results in association studies comes from population stratification and inappropriate sample sizes.
Moreover, parametric statistical tests are frequently used, when they might be inappropriate if the sample size is not large enough or the distribution is too skewed.
First, basic power calculations using data from the National Trauma Data Bank of the American College of Surgeons [2] suggest that reliably measuring a mortality difference would require over 5,000 patients rendering the first outcome measure inappropriate given the combined sample size of all published studies.
These included inappropriate outcomes studied, small sample size and the medication review being too non-committal as well as the heterogeneity of the design of medication reviews.
Many microarray-based molecular studies have been criticized as noisy discovery due to problems such as small sample size, inappropriate statistical analysis leading to over-fitting of data, lack of independent validation, or validation with too small set [ 9, 36, 37].
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