Changes

==Sample size calculation==

==Sample size calculation==

As mentioned earlier, it is important in any study not only that bias is minimised, but that the sample has sufficient [[Random variation#Confidence intervals and study precision|precision]] (in the case of descriptive studies) or [[Random variation#Hypothesis testing and study power|power]] (in the case of analytic studies). Both of these are closely related to the [[Random variation|random variability]] in any sample taken from a population. Although this can be reduced by increasing the sample size, a number of other considerations (usually logistical and economic considerations) will also be acting in order to reduce the number of samples which can realistically be taken. Statistical techniques are therefore available in order to calculate the required sample size. However, counterintuitively, these require assumptions to be made regarding the final results of the study.

As mentioned earlier, it is important in any study not only that bias is minimised, but that the sample has sufficient [[Random variation#Confidence intervals and study precision|precision]] (in the case of descriptive studies) or [[Random variation#Hypothesis testing and study power|power]] (in the case of analytic studies). Both of these are closely related to the [[Random variation|random variability]] in any sample taken from a population. Although this can be reduced by increasing the sample size, a number of other considerations (usually logistical and economic considerations) will also be acting in order to reduce the number of samples which can realistically be taken. Statistical techniques are therefore available in order to calculate the required sample size. Counterintuitively, these require assumptions to be made regarding the final results of the study, as well as information regarding the required level of confidence, precision or power of the study.

 

The variability of an outcome of interest in the sample collected will have a considerable effect on the precision and power of a study. When the outcome is a continuous variable, this variability can be measured as the variance in the source population. However, in the case of binary outcomes, the concept of variability can be more difficult to comprehend. In these cases, the binomial distribution is used to estimate the variance - calculated as the proportion of animals with the outcome of interest multiplied by the proportion of animals without the outcome of interest. This can be viewed as the expected variation in the proportion estimate of a sample if a number of samples were repeatedly taken from the source population, rather than the variation in the proportion estimate in the source population itself.

 

[[Category:Veterinary Epidemiology - Introduction|F]]

[[Category:Veterinary Epidemiology - Introduction|F]]