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Current statistical principles demand that you use a method called Hierarchical Linear Modeling (HLM) to analyze the data, and unless the effect size is very large, 20 schools will not be sufficient for statistical significance.
This sample size was deemed to be sufficient for statistical analysis.
Survival will also be monitored, but the number of prostate cancer deaths is unlikely to be sufficient for statistical analysis.
In this situation, the number of mapped reads will not be sufficient for statistical analysis: for example, when 20 million reads are sequenced and the mapping ratio is 15%, only 3 million reads are available for analysis.
Indeed, although power calculation showed that a sample size of 166 would be sufficient for statistical purposes, a bigger sample size is needed in order to obtain representative results.
However, the lower than feared loss to follow-up rate left us with a final sample size that actually exceeded the 3360 we predicted to be sufficient for statistical power, so the impact of this was minimal.
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Ensure that the number of test and control sites is sufficient for statistical significance.
Although the amount of data is sufficient for statistical analysis, there are also some inherent limitations.
Only 76 patients were analysed for survival rates (the follow-up time was sufficient for statistical analysis, >3 years).
Also, our study is limited by incomplete assignment of discriminatory peaks using MS/MS, and by the sample size which is sufficient for statistical significance yet relatively small.
From this, it follows that the actual sample size of the PROMO study should be sufficient for adequate statistical power, taking potential effect modification into account.
More suggestions(15)
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