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The sample size was calculated by estimating a proportion in a population-based survey.
This approach has reasonable frequentist properties when estimating a proportion (Cai, 2005).
Sample size was calculated based on estimating a proportion in a population-based survey.
Because Proc Descript does not provide design effect estimates for distribution percentiles, we multiplied the design effect associated with a mean by 30 or 80 [i.e., the NCHS-recommended sample size for estimating a proportion of 0.50 (n = 30) or a proportion of 0.10 or 0.90 (n = 80) when the design effect is 1.0 (NCHS 2004b)].
For instance, for estimating a proportion P x, the general form of the estimate is where the summations are over all sample participants, w i denotes the sampling weight associated with participant-period (or house-hold-period) i, and X i is an indicator variable with a value of 1 if participant-period i has the characteristic of interest and with a value of 0 otherwise.
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The size of a sample needed for statistical generalizability depends, among other factors, on how pre-chosen variables are distributed, whether a study aims to estimate a proportion or find a causal relationship, and the total number of pre-selected variables in the model.
Then, we estimated a proportion of NUPTs that are also similar to the mitochondrial genome.
Thus, we first estimated a proportion of sequences similar to both chloroplast and mitochondrial genomes (Table 1).
It was calculated to estimate a proportion, assuming an expected preference for oral treatment of 43% with an alpha risk of 0.05 and a precision 0.4.
With this sample can estimate a proportion of 20% of false diagnosis of COPD in a electronic medical records, with a precision of ± 0.3%.
The sample size software nQuery Advisor version 6 was used to conduct a sample size calculation based on the primary outcome to estimate a proportion with 95% confidence intervals to a power of 90%.
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