700
edits
Changes
no edit summary
===Power===
===Power===
This relates to the ability to detect a difference in a parameter of interest between two groups, and so relates to analytic studies using hypothesis testing. The power indicates the probability that a study will detect a difference between groups, assuming that a difference of a specified size does exist, at a specified level of confidence. - for example, if
This relates to the ability to detect a difference in a parameter of interest between two groups, and so relates to analytic studies. The power indicates the probability that a study will detect a 'significant' difference between groups (using a specified p-value [usually 0.05] to indicate significance), assuming that a difference of a specified size does exist. For example, if there is a true difference in mean annual milk yield of 500 litres between two groups of cows, a study with a power of 80% will detect a statistically significant difference 80% of the time. That is, if the same study was repeated again and again, selecting the calculated required number of cows from each herd, 80% of these studies would detect a difference between groups and 20% would not.
===Clustering===
===Clustering===
When cluster or multistage sampling techniques are used, the effect of clustering of the outcome of interest within clusters will have an effect on the required sample size, since animals within the same cluster would be expected to be more similar to each other than to those from other clusters. Therefore, formulas are available in order to calculate the 'design effect' (or DEFF), which indicates the factor by which the calculated sample size needs to be increased by in order to account for this.
===Multivariate studies===
[[Category:Veterinary Epidemiology - Introduction|F]]
[[Category:Veterinary Epidemiology - Introduction|F]]